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2020 – today
- 2025
[j96]Zhuo Huang, Muyang Li, Li Shen, Jun Yu
, Chen Gong, Bo Han, Tongliang Liu:
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. Int. J. Comput. Vis. 133(1): 456-474 (2025)- [j95]Xu Zhang, Zhe Chen, Jing Zhang, Tongliang Liu, Dacheng Tao:
Learning General and Specific Embedding with Transformer for Few-Shot Object Detection. Int. J. Comput. Vis. 133(2): 968-984 (2025) - [j94]Zhongyi Han, Gongxu Luo, Hao Sun, Yaqian Li, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu:
Alignclip: navigating the misalignments for robust vision-language generalization. Mach. Learn. 114(3): 58 (2025) - [j93]Wenshui Luo, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong:
Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(1): 305-322 (2025) - [i221]Runnan Chen, Xiangyu Sun, Zhaoqing Wang, Youquan Liu, Jiepeng Wang, Lingdong Kong, Jiankang Deng, Mingming Gong, Liang Pan, Wenping Wang, Tongliang Liu:
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies. CoRR abs/2501.00326 (2025) - [i220]Runnan Chen, Zhaoqing Wang, Jiepeng Wang, Yuexin Ma, Mingming Gong, Wenping Wang, Tongliang Liu:
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM. CoRR abs/2501.00352 (2025) - [i219]Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tom Weidong Cai, Tongliang Liu:
NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References. CoRR abs/2501.06488 (2025) - [i218]Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, Tongliang Liu:
Flow: A Modular Approach to Automated Agentic Workflow Generation. CoRR abs/2501.07834 (2025) - [i217]Zhiqiang Kou, Si Qin, Hailin Wang, Ming-Kun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng:
Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation. CoRR abs/2502.01170 (2025) - [i216]Xingjun Ma, Yifeng Gao, Yixu Wang, Ruofan Wang, Xin Wang, Ye Sun, Yifan Ding, Hengyuan Xu, Yunhao Chen, Yunhan Zhao, Hanxun Huang, Yige Li, Jiaming Zhang, Xiang Zheng, Yang Bai, Zuxuan Wu, Xipeng Qiu, Jingfeng Zhang, Yiming Li, Jun Sun, Cong Wang, Jindong Gu, Baoyuan Wu, Siheng Chen, Tianwei Zhang, Yang Liu, Mingming Gong, Tongliang Liu, Shirui Pan, Cihang Xie, Tianyu Pang, Yinpeng Dong, Ruoxi Jia, Yang Zhang, Shiqing Ma, Xiangyu Zhang, Neil Gong, Chaowei Xiao, Sarah M. Erfani, Bo Li, Masashi Sugiyama, Dacheng Tao, James Bailey, Yu-Gang Jiang:
Safety at Scale: A Comprehensive Survey of Large Model Safety. CoRR abs/2502.05206 (2025) - [i215]Suqin Yuan, Runqi Lin, Lei Feng, Bo Han, Tongliang Liu:
Instance-dependent Early Stopping. CoRR abs/2502.07547 (2025) - [i214]Suqin Yuan, Lei Feng, Tongliang Liu:
Early Stopping Against Label Noise Without Validation Data. CoRR abs/2502.07551 (2025) - [i213]Suqin Yuan, Lei Feng, Bo Han, Tongliang Liu:
Enhancing Sample Selection by Cutting Mislabeled Easy Examples. CoRR abs/2502.08227 (2025) - [i212]Ziming Hong, Yongli Xiang, Tongliang Liu:
Toward Robust Non-Transferable Learning: A Survey and Benchmark. CoRR abs/2502.13593 (2025) - [i211]Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han:
Noisy Test-Time Adaptation in Vision-Language Models. CoRR abs/2502.14604 (2025) - 2024
- [j92]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. J. Mach. Learn. Res. 25: 154:1-154:50 (2024) - [j91]Jialiang Shen, Yu Yao, Shaoli Huang, Zhiyong Wang, Jing Zhang, Ruxing Wang, Jun Yu, Tongliang Liu:
ProtoSimi: label correction for fine-grained visual categorization. Mach. Learn. 113(4): 1903-1920 (2024) - [j90]DeLiang Wang, Mauro Forti, Tongliang Liu, Taro Toyoizumi:
Expansion of the editorial team. Neural Networks 173: 106209 (2024) - [j89]Sichao Fu, Xueqi Ma, Yibing Zhan, Fanyu You, Qinmu Peng, Tongliang Liu, James Bailey, Danilo P. Mandic:
Finding core labels for maximizing generalization of graph neural networks. Neural Networks 180: 106635 (2024) - [j88]Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3522-3536 (2024) - [j87]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4398-4409 (2024) - [j86]Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu:
A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4830-4842 (2024) - [j85]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels With Network Parameter Additive Decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 46(9): 6341-6354 (2024) - [j84]Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu:
Transferring Annotator- and Instance-Dependent Transition Matrix for Learning From Crowds. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7377-7391 (2024) - [j83]Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, Dacheng Tao:
Continual Learning From a Stream of APIs. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 11432-11445 (2024) - [j82]Yulong Yang, Chenhao Lin, Qian Li, Zhengyu Zhao, Haoran Fan, Dawei Zhou, Nannan Wang, Tongliang Liu, Chao Shen:
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization. IEEE Trans. Inf. Forensics Secur. 19: 3265-3278 (2024) - [j81]Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. Trans. Mach. Learn. Res. 2024 (2024) - [j80]Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu:
Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification. IEEE Trans. Multim. 26: 4206-4216 (2024) - [j79]Mingyu Li, Tao Zhou, Bo Han, Tongliang Liu, Xinkai Liang, Jiajia Zhao, Chen Gong:
Class-Wise Contrastive Prototype Learning for Semi-Supervised Classification Under Intersectional Class Mismatch. IEEE Trans. Multim. 26: 8145-8156 (2024) - [j78]Liangchen Liu, Nannan Wang, Decheng Liu, Xi Yang, Xinbo Gao, Tongliang Liu:
Towards Specific Domain Prompt Learning via Improved Text Label Optimization. IEEE Trans. Multim. 26: 10805-10815 (2024) - [j77]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 35(11): 16683-16695 (2024) - [j76]Tianfu Wang, Li Shen, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong:
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning. IEEE Trans. Serv. Comput. 17(3): 1001-1015 (2024) - [c187]Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu:
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning. AAAI 2024: 4632-4640 - [c186]Rundong He, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu, Yongshun Gong:
Exploring Channel-Aware Typical Features for Out-of-Distribution Detection. AAAI 2024: 12402-12410 - [c185]Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li:
One-Shot Learning as Instruction Data Prospector for Large Language Models. ACL (1) 2024: 4586-4601 - [c184]Lianyang Ma, Yu Yao, Tao Liang, Tongliang Liu:
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos. AI (2) 2024: 281-297 - [c183]Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu:
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance. CIKM 2024: 270-280 - [c182]Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, Jieping Ye:
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning. CVPR 2024: 18504-18515 - [c181]Ziming Hong, Li Shen, Tongliang Liu:
Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning. CVPR 2024: 28805-28815 - [c180]Zhenyi Wang, Li Shen, Junfeng Guo, Tiehang Duan, Siyu Luan, Tongliang Liu, Mingchen Gao:
Training A Secure Model Against Data-Free Model Extraction. ECCV (79) 2024: 323-340 - [c179]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. ICLR 2024 - [c178]Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang, Mingming Gong, Tongliang Liu:
Improving Non-Transferable Representation Learning by Harnessing Content and Style. ICLR 2024 - [c177]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. ICLR 2024 - [c176]Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu:
Neural Auto-designer for Enhanced Quantum Kernels. ICLR 2024 - [c175]Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c174]Xiu-Chuan Li, Kun Zhang, Tongliang Liu:
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions. ICLR 2024 - [c173]Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu:
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. ICLR 2024 - [c172]Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian:
Out-of-Distribution Detection with Negative Prompts. ICLR 2024 - [c171]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. ICLR 2024 - [c170]Suqin Yuan, Lei Feng, Tongliang Liu:
Early Stopping Against Label Noise Without Validation Data. ICLR 2024 - [c169]Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu:
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. ICLR 2024 - [c168]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. ICLR 2024 - [c167]Jiyang Zheng, Yu Yao, Bo Han, Dadong Wang, Tongliang Liu:
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation. ICLR 2024 - [c166]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. ICLR 2024 - [c165]Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han:
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. ICML 2024 - [c164]Yusong Hu, De Cheng, Dingwen Zhang, Nannan Wang, Tongliang Liu, Xinbo Gao:
Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning. ICML 2024 - [c163]Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu:
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning. ICML 2024 - [c162]Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu:
Towards Realistic Model Selection for Semi-supervised Learning. ICML 2024 - [c161]Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. ICML 2024 - [c160]Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han:
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. ICML 2024 - [c159]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. ICML 2024 - [c158]Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu:
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. ICML 2024 - [c157]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graphs via Topological Sample Selection. ICML 2024 - [c156]Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu:
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints. ICML 2024 - [c155]Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu:
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training. ICML 2024 - [c154]Haoang Chi, He Li, Wenjing Yang, Feng Liu, Long Lan, Xiaoguang Ren, Tongliang Liu, Bo Han:
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? NeurIPS 2024 - [c153]Dongting Hu, Huan Fu, Jiaxian Guo, Liuhua Peng, Tingjin Chu, Feng Liu, Tongliang Liu, Mingming Gong:
In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment. NeurIPS 2024 - [c152]Yexiong Lin, Yu Yao, Tongliang Liu:
Learning the Latent Causal Structure for Modeling Label Noise. NeurIPS 2024 - [c151]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. NeurIPS 2024 - [c150]Xiong Peng, Bo Han, Feng Liu, Tongliang Liu, Mingyuan Zhou:
Pseudo-Private Data Guided Model Inversion Attacks. NeurIPS 2024 - [c149]Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han:
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. NeurIPS 2024 - [c148]Haoyu Wang, Zhuo Huang, Zhiwei Lin, Tongliang Liu:
NoiseGPT: Label Noise Detection and Rectification through Probability Curvature. NeurIPS 2024 - [c147]Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han:
What If the Input is Expanded in OOD Detection? NeurIPS 2024 - [c146]Hongling Zheng, Li Shen, Yong Luo, Tongliang Liu, Jialie Shen, Dacheng Tao:
Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization. NeurIPS 2024 - [c145]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. NeurIPS 2024 - [e2]Tongliang Liu, Geoffrey I. Webb, Lin Yue, Dadong Wang:
AI 2023: Advances in Artificial Intelligence - 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28 - December 1, 2023, Proceedings, Part I. Lecture Notes in Computer Science 14471, Springer 2024, ISBN 978-981-99-8387-2 [contents] - [e1]Tongliang Liu, Geoffrey I. Webb, Lin Yue, Dadong Wang:
AI 2023: Advances in Artificial Intelligence - 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28 - December 1, 2023, Proceedings, Part II. Lecture Notes in Computer Science 14472, Springer 2024, ISBN 978-981-99-8390-2 [contents] - [i210]Xue Dong, Xuemeng Song, Tongliang Liu, Weili Guan:
Prompt-based Multi-interest Learning Method for Sequential Recommendation. CoRR abs/2401.04312 (2024) - [i209]Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu:
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning. CoRR abs/2401.08117 (2024) - [i208]Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang:
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. CoRR abs/2401.09716 (2024) - [i207]Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu:
Neural auto-designer for enhanced quantum kernels. CoRR abs/2401.11098 (2024) - [i206]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. CoRR abs/2402.03941 (2024) - [i205]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. CoRR abs/2402.07011 (2024) - [i204]Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu:
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision. CoRR abs/2402.08960 (2024) - [i203]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. CoRR abs/2402.13241 (2024) - [i202]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. CoRR abs/2402.14430 (2024) - [i201]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. CoRR abs/2402.15070 (2024) - [i200]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graph via Topological Sample Selection. CoRR abs/2403.01942 (2024) - [i199]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. CoRR abs/2403.08840 (2024) - [i198]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels with Network Parameter Additive Decomposition. CoRR abs/2403.13241 (2024) - [i197]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. CoRR abs/2403.14774 (2024) - [i196]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. CoRR abs/2403.20078 (2024) - [i195]Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, Xiang Wan:
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification. CoRR abs/2404.06795 (2024) - [i194]Runqi Lin, Chaojian Yu, Tongliang Liu:
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization. CoRR abs/2404.08154 (2024) - [i193]Yuxiang Zheng, Zhongyi Han, Yilong Yin, Xin Gao, Tongliang Liu:
Can We Treat Noisy Labels as Accurate? CoRR abs/2405.12969 (2024) - [i192]Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Xiaobo Xia, Tongliang Liu, Min Yang, Binyuan Hui:
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception. CoRR abs/2405.15232 (2024) - [i191]Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. CoRR abs/2405.16262 (2024) - [i190]Hongwei Bran Li, Fernando Navarro, Ivan Ezhov, Amirhossein Bayat, Dhritiman Das, Florian Kofler, Suprosanna Shit, Diana Waldmannstetter, Johannes C. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, Jonas Weidner, Michelle Alonso-Basanta, Arif Rashid, Ujjwal Baid, Wesam Adel, Deniz Alis, Bhakti Baheti, Yingbin Bai, Ishaan Bhat, Sabri Can Cetindag, Wenting Chen, Li Cheng, Prasad Dutande, Lara Dular, Mustafa A. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, Shubham Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, Hoang Long Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, Ilkay Öksüz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, Anindo Saha, Raghavendra Selvan, Linlin Shen, João Lourenço Silva, Ziga Spiclin, Sanjay N. Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, Mert Yergin, Shuang Yu, Lingxi Zeng, YingLin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard K. G. Do, Anton S. Becker, Amber L. Simpson, Ender Konukoglu, András Jakab, Spyridon Bakas, Leo Joskowicz, Bjoern H. Menze:
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge. CoRR abs/2405.18435 (2024) - [i189]Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han:
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. CoRR abs/2405.18786 (2024) - [i188]Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu:
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. CoRR abs/2405.19919 (2024) - [i187]Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu:
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training. CoRR abs/2406.00685 (2024) - [i186]Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han:
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. CoRR abs/2406.00806 (2024) - [i185]Xiaoli Wei, Zhaoqing Wang, Yandong Guo, Chunxia Zhang, Tongliang Liu, Mingming Gong:
Training-Free Robust Interactive Video Object Segmentation. CoRR abs/2406.05485 (2024) - [i184]Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama:
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR abs/2406.09179 (2024) - [i183]Tianfu Wang, Li Shen, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong:
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning. CoRR abs/2406.17334 (2024) - [i182]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR abs/2407.10132 (2024) - [i181]Huaxi Huang, Xin Yu, Qiyu Liao, Dadong Wang, Tongliang Liu:
Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning. CoRR abs/2409.03326 (2024) - [i180]Yewen Li, Chaojie Wang, Xiaobo Xia, Xu He, Ruyi An, Dong Li, Tongliang Liu, Bo An, Xinrun Wang:
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection. CoRR abs/2409.03801 (2024) - [i179]Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han:
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. CoRR abs/2410.12474 (2024) - [i178]Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han:
What If the Input is Expanded in OOD Detection? CoRR abs/2410.18472 (2024) - [i177]Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han:
Model Inversion Attacks: A Survey of Approaches and Countermeasures. CoRR abs/2411.10023 (2024) - [i176]Zhaoqing Wang, Xiaobo Xia, Runnan Chen, Dongdong Yu, Changhu Wang, Mingming Gong, Tongliang Liu:
LaVin-DiT: Large Vision Diffusion Transformer. CoRR abs/2411.11505 (2024) - [i175]Haodong Chen, Runnan Chen, Qiang Qu, Zhaoqing Wang, Tongliang Liu, Xiaoming Chen, Yuk Ying Chung:
Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels. CoRR abs/2411.12440 (2024) - [i174]Zhenchen Wan, Yanwu Xu, Zhaoqing Wang, Feng Liu, Tongliang Liu, Mingming Gong:
TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On. CoRR abs/2411.17017 (2024) - [i173]Yuxin Tian, Mouxing Yang, Yuhao Zhou, Jian Wang, Qing Ye, Tongliang Liu, Gang Niu, Jiancheng Lv:
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels. CoRR abs/2412.00452 (2024) - [i172]Ziwen Li, Jiaxin Huang, Runnan Chen, Yunlong Che, Yandong Guo, Tongliang Liu, Fakhri Karray, Mingming Gong:
Urban4D: Semantic-Guided 4D Gaussian Splatting for Urban Scene Reconstruction. CoRR abs/2412.03473 (2024) - [i171]Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian:
Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty. CoRR abs/2412.05897 (2024) - [i170]Weijie Tu, Weijian Deng, Dylan Campbell, Yu Yao, Jiyang Zheng, Tom Gedeon, Tongliang Liu:
Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels. CoRR abs/2412.06461 (2024) - 2023
- [j75]Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu:
On exploring node-feature and graph-structure diversities for node drop graph pooling. Neural Networks 167: 559-571 (2023) - [j74]Xiaobo Xia, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, Tongliang Liu:
Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3047-3058 (2023) - [j73]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
Handling Open-Set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 9846-9861 (2023) - [j72]Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao:
Recent Advances for Quantum Neural Networks in Generative Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(10): 12321-12340 (2023) - [j71]Shuo Yang, Songhua Wu, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
A Parametrical Model for Instance-Dependent Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14055-14068 (2023) - [j70]Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu:
Dynamics-aware loss for learning with label noise. Pattern Recognit. 144: 109835 (2023) - [j69]Shenghong He, Ruxin Wang, Tongliang Liu, Chao Yi, Xin Jin, Renyang Liu, Wei Zhou:
Type-I Generative Adversarial Attack. IEEE Trans. Dependable Secur. Comput. 20(3): 2593-2606 (2023) - [j68]Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang:
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. Trans. Mach. Learn. Res. 2023 (2023) - [j67]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
FedDAG: Federated DAG Structure Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j66]Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han:
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. Trans. Mach. Learn. Res. 2023 (2023) - [j65]Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge:
Trustable Co-Label Learning From Multiple Noisy Annotators. IEEE Trans. Multim. 25: 1045-1057 (2023) - [j64]Jie Ma, Jun Liu, Yaxian Wang, Junjun Li, Tongliang Liu:
Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering. IEEE Trans. Neural Networks Learn. Syst. 34(1): 15-27 (2023) - [j63]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
An Optimal Transport Analysis on Generalization in Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34(6): 2842-2853 (2023) - [j62]Yu Yao, Baosheng Yu, Chen Gong, Tongliang Liu:
Understanding How Pretraining Regularizes Deep Learning Algorithms. IEEE Trans. Neural Networks Learn. Syst. 34(9): 5828-5840 (2023) - [c144]Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao:
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning. CVPR 2023: 7736-7745 - [c143]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CVPR 2023: 16175-16185 - [c142]Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao:
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting. CVPR 2023: 19348-19357 - [c141]Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu:
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency. CVPR 2023: 19883-19892 - [c140]Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu:
Holistic Label Correction for Noisy Multi-Label Classification. ICCV 2023: 1483-1493 - [c139]Chengxin Liu, Hao Lu, Zhiguo Cao, Tongliang Liu:
Point-Query Quadtree for Crowd Counting, Localization, and More. ICCV 2023: 1676-1685 - [c138]Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu:
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. ICCV 2023: 1833-1843 - [c137]Kaicheng Yang, Jiankang Deng, Xiang An, Jiawei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu:
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption. ICCV 2023: 2910-2919 - [c136]Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu:
HumanMAC: Masked Motion Completion for Human Motion Prediction. ICCV 2023: 9510-9521 - [c135]Suqin Yuan, Lei Feng, Tongliang Liu:
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples. ICCV 2023: 16033-16042 - [c134]Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu:
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. ICCV 2023: 16673-16684 - [c133]Dongting Hu, Zhenkai Zhang, Tingbo Hou, Tongliang Liu, Huan Fu, Mingming Gong:
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering. ICCV 2023: 17726-17737 - [c132]Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu:
Unicom: Universal and Compact Representation Learning for Image Retrieval. ICLR 2023 - [c131]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. ICLR 2023 - [c130]Shuxian Liang, Xu Shen, Tongliang Liu, Xian-Sheng Hua:
Contextual Convolutional Networks. ICLR 2023 - [c129]Yong Lin, Renjie Pi, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han:
A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. ICLR 2023 - [c128]Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, Tongliang Liu:
Mosaic Representation Learning for Self-supervised Visual Pre-training. ICLR 2023 - [c127]Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao:
Symmetric Pruning in Quantum Neural Networks. ICLR 2023 - [c126]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. ICLR 2023 - [c125]Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu:
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. ICLR 2023 - [c124]Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han:
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. ICLR 2023 - [c123]Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang:
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. ICML 2023: 4611-4622 - [c122]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c121]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Detecting Out-of-distribution Data through In-distribution Class Prior. ICML 2023: 15067-15088 - [c120]Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. ICML 2023: 36804-36820 - [c119]Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu:
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023: 39660-39673 - [c118]Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu:
Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration. ICML 2023: 42517-42530 - [c117]Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu:
Phase-aware Adversarial Defense for Improving Adversarial Robustness. ICML 2023: 42724-42741 - [c116]Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han:
Exploring Model Dynamics for Accumulative Poisoning Discovery. ICML 2023: 42983-43004 - [c115]Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han:
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. ICML 2023: 43068-43104 - [c114]Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, Dacheng Tao:
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities. IJCAI 2023: 6712-6722 - [c113]Wenjie Xuan, Shanshan Zhao, Yu Yao, Juhua Liu, Tongliang Liu, Yixin Chen, Bo Du, Dacheng Tao:
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions. ACM Multimedia 2023: 1924-1932 - [c112]Yingbin Bai, Zhongyi Han, Erkun Yang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu:
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. NeurIPS 2023 - [c111]Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, Wenping Wang:
Towards Label-free Scene Understanding by Vision Foundation Models. NeurIPS 2023 - [c110]Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu:
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. NeurIPS 2023 - [c109]Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu:
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. NeurIPS 2023 - [c108]Runqi Lin, Chaojian Yu, Tongliang Liu:
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization. NeurIPS 2023 - [c107]Yexiong Lin, Yu Yao, Xiaolong Shi, Mingming Gong, Xu Shen, Dong Xu, Tongliang Liu:
CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation. NeurIPS 2023 - [c106]Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David S. Doermann, Mingchen Gao:
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training. NeurIPS 2023 - [c105]Enneng Yang, Li Shen, Zhenyi Wang, Tongliang Liu, Guibing Guo:
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning. NeurIPS 2023 - [c104]Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han:
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. NeurIPS 2023 - [c103]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS 2023 - [c102]Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS 2023 - [i169]Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu:
HumanMAC: Masked Motion Completion for Human Motion Prediction. CoRR abs/2302.03665 (2023) - [i168]Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han:
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. CoRR abs/2303.00250 (2023) - [i167]Jiren Mai, Fei Zhang, Junjie Ye, Marcus Kalander, Xian Zhang, Wankou Yang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. CoRR abs/2303.02449 (2023) - [i166]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. CoRR abs/2303.05033 (2023) - [i165]Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao:
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning. CoRR abs/2303.11183 (2023) - [i164]Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu:
Dynamics-Aware Loss for Learning with Label Noise. CoRR abs/2303.11562 (2023) - [i163]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i162]Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu:
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency. CoRR abs/2303.12419 (2023) - [i161]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CoRR abs/2303.13087 (2023) - [i160]Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu:
Unicom: Universal and Compact Representation Learning for Image Retrieval. CoRR abs/2304.05884 (2023) - [i159]Dongting Hu, Zhenkai Zhang, Tingbo Hou, Tongliang Liu, Huan Fu, Mingming Gong:
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering. CoRR abs/2304.10075 (2023) - [i158]Yulong Yang, Chenhao Lin, Qian Li, Chao Shen, Dawei Zhou, Nannan Wang, Tongliang Liu:
Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths. CoRR abs/2305.05875 (2023) - [i157]Bochao Liu, Shiming Ge, Pengju Wang, Liansheng Zhuang, Tongliang Liu:
Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation. CoRR abs/2305.10662 (2023) - [i156]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR abs/2305.18377 (2023) - [i155]Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao:
DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Text Spotting. CoRR abs/2305.19957 (2023) - [i154]Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu:
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds. CoRR abs/2306.03116 (2023) - [i153]Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han:
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. CoRR abs/2306.03715 (2023) - [i152]Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han:
Exploring Model Dynamics for Accumulative Poisoning Discovery. CoRR abs/2306.03726 (2023) - [i151]Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, Wenping Wang:
Towards Label-free Scene Understanding by Vision Foundation Models. CoRR abs/2306.03899 (2023) - [i150]Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang:
Advancing Counterfactual Inference through Quantile Regression. CoRR abs/2306.05751 (2023) - [i149]Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang:
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. CoRR abs/2306.06931 (2023) - [i148]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i147]Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. CoRR abs/2306.11343 (2023) - [i146]Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu:
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling. CoRR abs/2306.12726 (2023) - [i145]Vinoth Nandakumar, Peng Mi, Tongliang Liu:
Why can neural language models solve next-word prediction? A mathematical perspective. CoRR abs/2306.17184 (2023) - [i144]Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Tianshuo Xu, Xiaoshuai Sun, Tongliang Liu, Rongrong Ji, Dacheng Tao:
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer. CoRR abs/2306.17504 (2023) - [i143]Vinoth Nandakumar, Arush Tagade, Tongliang Liu:
Why do CNNs excel at feature extraction? A mathematical explanation. CoRR abs/2307.00919 (2023) - [i142]Hui Kang, Sheng Liu, Huaxi Huang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu:
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels. CoRR abs/2307.05025 (2023) - [i141]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR abs/2307.05948 (2023) - [i140]Wenjie Xuan, Shanshan Zhao, Yu Yao, Juhua Liu, Tongliang Liu, Yixin Chen, Bo Du, Dacheng Tao:
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions. CoRR abs/2307.14070 (2023) - [i139]Hui Kang, Sheng Liu, Huaxi Huang, Tongliang Liu:
Channel-Wise Contrastive Learning for Learning with Noisy Labels. CoRR abs/2308.06952 (2023) - [i138]Kaicheng Yang, Jiankang Deng, Xiang An, Jiawei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu:
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption. CoRR abs/2308.08428 (2023) - [i137]Yuxuan Du, Yibo Yang, Tongliang Liu, Zhouchen Lin, Bernard Ghanem, Dacheng Tao:
ShadowNet for Data-Centric Quantum System Learning. CoRR abs/2308.11290 (2023) - [i136]Chengxin Liu, Hao Lu, Zhiguo Cao, Tongliang Liu:
Point-Query Quadtree for Crowd Counting, Localization, and More. CoRR abs/2308.13814 (2023) - [i135]Suqin Yuan, Lei Feng, Tongliang Liu:
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples. CoRR abs/2308.13862 (2023) - [i134]Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, Dacheng Tao:
Continual Learning From a Stream of APIs. CoRR abs/2309.00023 (2023) - [i133]Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-estimators for Learning with Noisy Labels. CoRR abs/2309.00894 (2023) - [i132]Liangchen Liu, Nannan Wang, Dawei Zhou, Xinbo Gao, Decheng Liu, Xi Yang, Tongliang Liu:
Gradient constrained sharpness-aware prompt learning for vision-language models. CoRR abs/2309.07866 (2023) - [i131]Shikun Li, Xiaobo Xia, Hansong Zhang, Shiming Ge, Tongliang Liu:
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm. CoRR abs/2309.12706 (2023) - [i130]Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, Tongliang Liu, Wenping Wang:
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training. CoRR abs/2309.16956 (2023) - [i129]Chaojian Yu, Xiaolong Shi, Jun Yu, Bo Han, Tongliang Liu:
On the Onset of Robust Overfitting in Adversarial Training. CoRR abs/2310.00607 (2023) - [i128]Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han:
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. CoRR abs/2310.05077 (2023) - [i127]Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu:
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. CoRR abs/2310.08847 (2023) - [i126]Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu:
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. CoRR abs/2310.10873 (2023) - [i125]Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR abs/2310.13923 (2023) - [i124]Zhuo Huang, Muyang Li, Li Shen, Jun Yu, Chen Gong, Bo Han, Tongliang Liu:
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. CoRR abs/2310.16391 (2023) - [i123]Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu:
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. CoRR abs/2310.16412 (2023) - [i122]Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu:
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. CoRR abs/2310.18910 (2023) - [i121]Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han:
DeepInception: Hypnotize Large Language Model to Be Jailbreaker. CoRR abs/2311.03191 (2023) - [i120]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR abs/2311.03236 (2023) - [i119]Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Tongliang Liu:
Coreset Selection with Prioritized Multiple Objectives. CoRR abs/2311.08675 (2023) - [i118]Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu:
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning. CoRR abs/2312.02546 (2023) - [i117]Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman H. Khan, Lina Yao, Tongliang Liu, Kun Zhang:
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation. CoRR abs/2312.07424 (2023) - [i116]Linghao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu:
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance. CoRR abs/2312.08852 (2023) - [i115]Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li:
One Shot Learning as Instruction Data Prospector for Large Language Models. CoRR abs/2312.10302 (2023) - 2022
- [j61]Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu:
A Machine Learning Approach for Predicting Human Preference for Graph Layouts. J. Graph Algorithms Appl. 26(1): 447-471 (2022) - [j60]Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama:
Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. 23: 307:1-307:34 (2022) - [j59]Xu Yang, Cheng Deng, Tongliang Liu, Dacheng Tao:
Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 44(4): 1992-2003 (2022) - [j58]Chen Gong, Qizhou Wang, Tongliang Liu, Bo Han, Jane You, Jian Yang, Dacheng Tao:
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4163-4177 (2022) - [j57]Hao Wang, Cheng Deng, Tongliang Liu, Dacheng Tao:
Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9181-9194 (2022) - [j56]Shuo Yang, Songhua Wu, Tongliang Liu, Min Xu:
Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9830-9843 (2022) - [j55]Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang:
COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognit. 124: 108499 (2022) - [j54]Jingchen Ke, Chen Gong, Tongliang Liu, Lin Zhao, Jian Yang, Dacheng Tao:
Laplacian Welsch Regularization for Robust Semisupervised Learning. IEEE Trans. Cybern. 52(1): 164-177 (2022) - [j53]Xinpeng Ding, Nannan Wang, Shiwei Zhang, Ziyuan Huang, Xiaomeng Li, Mingqian Tang, Tongliang Liu, Xinbo Gao:
Exploring Language Hierarchy for Video Grounding. IEEE Trans. Image Process. 31: 4693-4706 (2022) - [j52]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao:
Quantum Differentially Private Sparse Regression Learning. IEEE Trans. Inf. Theory 68(8): 5217-5233 (2022) - [j51]Long Lan, Tongliang Liu, Xiang Zhang, Chuanfu Xu, Zhigang Luo:
Label Propagated Nonnegative Matrix Factorization for Clustering. IEEE Trans. Knowl. Data Eng. 34(1): 340-351 (2022) - [j50]Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Quan Zhou, Tongliang Liu, Zongyuan Ge:
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation. IEEE Trans. Medical Imaging 41(6): 1533-1546 (2022) - [j49]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [j48]Zhaoyu Zhang, Mengyan Li, Haonian Xie, Jun Yu, Tongliang Liu, Chang Wen Chen:
TWGAN: Twin Discriminator Generative Adversarial Networks. IEEE Trans. Multim. 24: 677-688 (2022) - [j47]Zhengning Wu, Xiaobo Xia, Ruxin Wang, Jiatong Li, Jun Yu, Yinian Mao, Tongliang Liu:
LR-SVM+: Learning Using Privileged Information with Noisy Labels. IEEE Trans. Multim. 24: 1080-1092 (2022) - [j46]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5766-5774 (2022) - [j45]Shijun Cai, Seok-Hee Hong, Xiaobo Xia, Tongliang Liu, Weidong Huang:
A machine learning approach for predicting human shortest path task performance. Vis. Informatics 6(2): 50-61 (2022) - [c101]Amirmohammad Pasdar, Young Choon Lee, Tongliang Liu, Seok-Hee Hong:
Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices. CCGRID 2022: 239-248 - [c100]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c99]Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu:
Fair Classification with Instance-dependent Label Noise. CLeaR 2022: 927-943 - [c98]Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CVPR 2022: 316-325 - [c97]Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu:
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. CVPR 2022: 4032-4041 - [c96]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. CVPR 2022: 7022-7031 - [c95]Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng:
Mutual Quantization for Cross-Modal Search with Noisy Labels. CVPR 2022: 7541-7550 - [c94]Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu:
CRIS: CLIP-Driven Referring Image Segmentation. CVPR 2022: 11676-11685 - [c93]Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu:
Exploring Set Similarity for Dense Self-supervised Representation Learning. CVPR 2022: 16569-16578 - [c92]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c91]Xin Jin, Tianyu He, Xu Shen, Songhua Wu, Tongliang Liu, Jingwen Ye, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua:
Unleashing the Potential of Adaptation Models via Go-getting Domain Labels. ECCV Workshops (8) 2022: 308-325 - [c90]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c89]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c88]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. ICLR 2022 - [c87]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c86]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c85]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c84]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c83]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. ICLR 2022 - [c82]Joshua Y. Kim, Tongliang Liu, Kalina Yacef:
Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors. ICMI Companion 2022: 134-143 - [c81]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c80]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. ICML 2022: 25302-25312 - [c79]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. ICML 2022: 25595-25610 - [c78]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. ICML 2022: 27338-27352 - [c77]Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu:
Modeling Adversarial Noise for Adversarial Training. ICML 2022: 27353-27366 - [c76]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. IJCAI 2022: 3688-3694 - [c75]Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. KDD 2022: 1358-1367 - [c74]Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu:
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. KDD 2022: 2110-2119 - [c73]Xin Jin, Tianyu He, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua:
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification. ACM Multimedia 2022: 2163-2172 - [c72]Yong Luo, Ling-Yu Duan, Yan Bai, Tongliang Liu, Yihang Lou, Yonggang Wen:
Nonlinear Multi-Model Reuse. MMSP 2022: 1-6 - [c71]Amirmohammad Pasdar, Young Choon Lee, Seok-Hee Hong, Tongliang Liu:
MAPS: a dataset for semantic profiling and analysis of Android applications. MobiArch 2022: 13-18 - [c70]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c69]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS 2022 - [c68]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning. NeurIPS 2022 - [c67]De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu:
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. NeurIPS 2022 - [c66]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. NeurIPS 2022 - [c65]Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An:
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. NeurIPS 2022 - [c64]Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu:
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. NeurIPS 2022 - [c63]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attack Against Deep Neural Networks. NeurIPS 2022 - [c62]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [c61]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Gaussian Mixture Models. NeurIPS 2022 - [c60]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 2022 - [i114]Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu:
Do We Need to Penalize Variance of Losses for Learning with Label Noise? CoRR abs/2201.12739 (2022) - [i113]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i112]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i111]Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CoRR abs/2203.04181 (2022) - [i110]Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge:
Trustable Co-label Learning from Multiple Noisy Annotators. CoRR abs/2203.04199 (2022) - [i109]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. CoRR abs/2203.15202 (2022) - [i108]Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu:
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. CoRR abs/2203.15565 (2022) - [i107]Chuang Liu, Yibing Zhan, Chang Li, Bo Du, Jia Wu, Wenbin Hu, Tongliang Liu, Dacheng Tao:
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities. CoRR abs/2204.07321 (2022) - [i106]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Probabilistic Mixture-of-Experts. CoRR abs/2205.09086 (2022) - [i105]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i104]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i103]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. CoRR abs/2205.14826 (2022) - [i102]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
MSR: Making Self-supervised learning Robust to Aggressive Augmentations. CoRR abs/2206.01999 (2022) - [i101]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i100]Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao:
Recent Advances for Quantum Neural Networks in Generative Learning. CoRR abs/2206.03066 (2022) - [i99]Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. CoRR abs/2206.05483 (2022) - [i98]Lianyang Ma, Yu Yao, Tao Liang, Tongliang Liu:
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos. CoRR abs/2206.07981 (2022) - [i97]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. CoRR abs/2206.08675 (2022) - [i96]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. CoRR abs/2207.03162 (2022) - [i95]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. CoRR abs/2207.12203 (2022) - [i94]Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao:
Symmetric Pruning in Quantum Neural Networks. CoRR abs/2208.14057 (2022) - [i93]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attacks against Deep Neural Networks. CoRR abs/2209.14826 (2022) - [i92]Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu:
Strength-Adaptive Adversarial Training. CoRR abs/2210.01288 (2022) - [i91]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. CoRR abs/2210.05955 (2022) - [i90]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - [i89]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i88]Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao:
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting. CoRR abs/2211.10772 (2022) - [i87]Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu:
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. CoRR abs/2212.03462 (2022) - 2021
- [j44]Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao:
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. Int. J. Comput. Vis. 129(4): 1121-1138 (2021) - [j43]Chen Gong, Hong Shi, Tongliang Liu, Chuang Zhang, Jian Yang, Dacheng Tao:
Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(3): 918-932 (2021) - [j42]Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, Dacheng Tao:
Orthogonal Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(4): 1352-1368 (2021) - [j41]Jia Shao, Bo Du, Chen Wu, Mingming Gong, Tongliang Liu:
HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking. IEEE Trans. Image Process. 30: 3056-3068 (2021) - [j40]Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu:
KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization. IEEE Trans. Image Process. 30: 6869-6878 (2021) - [c59]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI 2021: 10183-10191 - [c58]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. AAAI 2021: 10192-10200 - [c57]Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu:
A Machine Learning Approach for Predicting Human Preference for Graph Layouts*. PacificVis 2021: 6-10 - [c56]Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua:
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework. CVPR 2021: 3579-3588 - [c55]Zhaowei Zhu, Tongliang Liu, Yang Liu:
A Second-Order Approach to Learning With Instance-Dependent Label Noise. CVPR 2021: 10113-10123 - [c54]Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu:
Removing Adversarial Noise in Class Activation Feature Space. ICCV 2021: 7858-7867 - [c53]Yingbin Bai, Tongliang Liu:
Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data. ICCV 2021: 9292-9301 - [c52]Jun Yu, Xinlong Hao, Zeyu Cui, Peng He, Tongliang Liu:
Boosting Fairness for Masked Face Recognition. ICCVW 2021: 1531-1540 - [c51]Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang:
Robust early-learning: Hindering the memorization of noisy labels. ICLR 2021 - [c50]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c49]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c48]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c47]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c46]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. ICML 2021: 11285-11295 - [c45]Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao:
Towards Defending against Adversarial Examples via Attack-Invariant Features. ICML 2021: 12835-12845 - [c44]Zhaoqing Wang, Xiangyu Kong, Zhanbei Cui, Ming Wu, Chuang Zhang, Mingming Gong, Tongliang Liu:
Vecnet: A Spectral and Multi-Scale Spatial Fusion Deep Network for Pixel-Level Cloud Type Classification in Himawari-8 Imagery. IGARSS 2021: 4083-4086 - [c43]Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge:
Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition. MICCAI (8) 2021: 3-12 - [c42]Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu:
Confident Anchor-Induced Multi-Source Free Domain Adaptation. NeurIPS 2021: 2848-2860 - [c41]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS 2021: 4409-4420 - [c40]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS 2021: 20970-20982 - [c39]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c38]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. NeurIPS 2021: 24392-24403 - [c37]Jiayu He, Matloob Khushi, Nguyen Hoang Tran, Tongliang Liu:
Robust Dual Recurrent Neural Networks for Financial Time Series Prediction. SDM 2021: 747-755 - [i86]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR abs/2101.05467 (2021) - [i85]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i84]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i83]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i82]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR abs/2102.04002 (2021) - [i81]Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, Zongyuan Ge:
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation. CoRR abs/2103.00528 (2021) - [i80]Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu:
A Machine Learning Approach for Predicting Human Preference for Graph Layouts. CoRR abs/2103.03665 (2021) - [i79]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. CoRR abs/2103.09468 (2021) - [i78]Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu:
Removing Adversarial Noise in Class Activation Feature Space. CoRR abs/2104.09197 (2021) - [i77]Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge:
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition. CoRR abs/2104.11057 (2021) - [i76]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Label-noise Transition Matrix using DNNs. CoRR abs/2105.13001 (2021) - [i75]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - [i74]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i73]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i72]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu:
Understanding (Generalized) Label Smoothing when Learning with Noisy Labels. CoRR abs/2106.04149 (2021) - [i71]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. CoRR abs/2106.04928 (2021) - [i70]Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao:
Towards Defending against Adversarial Examples via Attack-Invariant Features. CoRR abs/2106.05036 (2021) - [i69]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Jun Yu, Xiaoyu Wang, Tongliang Liu:
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training. CoRR abs/2106.05453 (2021) - [i68]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - [i67]Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Kwok-Wai Cheung:
KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation. CoRR abs/2106.06237 (2021) - [i66]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. CoRR abs/2106.06326 (2021) - [i65]Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang:
PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels. CoRR abs/2106.07451 (2021) - [i64]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i63]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. CoRR abs/2106.15853 (2021) - [i62]Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua:
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework. CoRR abs/2107.00181 (2021) - [i61]Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu:
Kernel Mean Estimation by Marginalized Corrupted Distributions. CoRR abs/2107.04855 (2021) - [i60]Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu:
Exploring Set Similarity for Dense Self-supervised Representation Learning. CoRR abs/2107.08712 (2021) - [i59]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR abs/2109.02986 (2021) - [i58]Dawei Zhou, Nannan Wang, Tongliang Liu, Bo Han:
Modelling Adversarial Noise for Adversarial Defense. CoRR abs/2109.09901 (2021) - [i57]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. CoRR abs/2110.12088 (2021) - [i56]Xin Jin, Tianyu He, Zhiheng Yin, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Xian-Sheng Hua, Zhibo Chen:
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification. CoRR abs/2111.10032 (2021) - [i55]Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu:
CRIS: CLIP-Driven Referring Image Segmentation. CoRR abs/2111.15174 (2021) - [i54]Joshua Yee Kim, Tongliang Liu, Kalina Yacef:
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors. CoRR abs/2112.03032 (2021) - [i53]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
Federated Causal Discovery. CoRR abs/2112.03555 (2021) - 2020
- [j39]Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Dinggang Shen, Jianping Yin, Wen Gao:
Multiple Kernel $k$k-Means with Incomplete Kernels. IEEE Trans. Pattern Anal. Mach. Intell. 42(5): 1191-1204 (2020) - [j38]Xinwang Liu, Lei Wang, Xinzhong Zhu, Miaomiao Li, En Zhu, Tongliang Liu, Li Liu, Yong Dou, Jianping Yin:
Absent Multiple Kernel Learning Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 42(6): 1303-1316 (2020) - [j37]Erkun Yang, Tongliang Liu, Cheng Deng, Dacheng Tao:
Adversarial Examples for Hamming Space Search. IEEE Trans. Cybern. 50(4): 1473-1484 (2020) - [j36]Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu:
Group Feedback Capsule Network. IEEE Trans. Image Process. 29: 6789-6799 (2020) - [j35]Yihang Lou, Ling-Yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen Gao:
Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm. IEEE Trans. Multim. 22(11): 3002-3013 (2020) - [j34]Cheng Deng, Erkun Yang, Tongliang Liu, Dacheng Tao:
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search. IEEE Trans. Neural Networks Learn. Syst. 31(6): 2189-2201 (2020) - [j33]Yang Wei, Chen Gong, Shuo Chen, Tongliang Liu, Jian Yang, Dacheng Tao:
Harnessing Side Information for Classification Under Label Noise. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3178-3192 (2020) - [j32]Fengxiang He, Tongliang Liu, Dacheng Tao:
Why ResNet Works? Residuals Generalize. IEEE Trans. Neural Networks Learn. Syst. 31(12): 5349-5362 (2020) - [c36]Maoying Qiao, Jun Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao:
Diversified Bayesian Nonnegative Matrix Factorization. AAAI 2020: 5420-5427 - [c35]Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich:
Generative-Discriminative Complementary Learning. AAAI 2020: 6526-6533 - [c34]Jiankang Deng, Jia Guo, Tongliang Liu, Mingming Gong, Stefanos Zafeiriou:
Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces. ECCV (11) 2020: 741-757 - [c33]Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao:
Learning with Bounded Instance and Label-dependent Label Noise. ICML 2020: 1789-1799 - [c32]Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao:
LTF: A Label Transformation Framework for Correcting Label Shift. ICML 2020: 3843-3853 - [c31]Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao:
Label-Noise Robust Domain Adaptation. ICML 2020: 10913-10924 - [c30]Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian:
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. ICML 2020: 11163-11172 - [c29]Shikang Gan, Yong Luo, Yonggang Wen, Tongliang Liu, Han Hu:
Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval. ACM Multimedia 2020: 1837-1845 - [c28]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS 2020 - [c27]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. NeurIPS 2020 - [c26]Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao:
Domain Generalization via Entropy Regularization. NeurIPS 2020 - [i52]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. CoRR abs/2001.03772 (2020) - [i51]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Towards Mixture Proportion Estimation without Irreducibility. CoRR abs/2002.03673 (2020) - [i50]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Multi-Class Classification from Noisy-Similarity-Labeled Data. CoRR abs/2002.06508 (2020) - [i49]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao, Nana Liu:
Quantum noise protects quantum classifiers against adversaries. CoRR abs/2003.09416 (2020) - [i48]Maoying Qiao, Tongliang Liu, Jun Yu, Wei Bian, Dacheng Tao:
Repulsive Mixture Models of Exponential Family PCA for Clustering. CoRR abs/2004.03112 (2020) - [i47]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. CoRR abs/2006.07805 (2020) - [i46]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A New Perspective on Learning with Label Noise. CoRR abs/2006.07831 (2020) - [i45]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR abs/2006.07836 (2020) - [i44]Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu:
Weakly Supervised Temporal Action Localization with Segment-Level Labels. CoRR abs/2007.01598 (2020) - [i43]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao:
Quantum differentially private sparse regression learning. CoRR abs/2007.11921 (2020) - [i42]Heliang Huang, Yuxuan Du, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, Rui Yang, Tongliang Liu, Min-Hsiu Hsieh, Hui Deng, Hao Rong, Cheng-Zhi Peng, Chao-Yang Lu, Yu-Ao Chen, Dacheng Tao, Xiaobo Zhu, Jian-Wei Pan:
Experimental Quantum Generative Adversarial Networks for Image Generation. CoRR abs/2010.06201 (2020) - [i41]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR abs/2010.11415 (2020) - [i40]Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama:
A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR abs/2011.04406 (2020) - [i39]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao:
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels. CoRR abs/2012.00932 (2020) - [i38]Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang:
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19. CoRR abs/2012.05509 (2020) - [i37]Zhaowei Zhu, Tongliang Liu, Yang Liu:
A Second-Order Approach to Learning with Instance-Dependent Label Noise. CoRR abs/2012.11854 (2020)
2010 – 2019
- 2019
- [j31]C. L. Philip Chen, Xinge You, Xinbo Gao, Tongliang Liu, Fionn Murtagh, Weifeng Liu:
Advances in data representation and learning for pattern analysis. Neurocomputing 348: 1-2 (2019) - [j30]Naiyang Guan, Tongliang Liu, Yangmuzi Zhang, Dacheng Tao, Larry S. Davis:
Truncated Cauchy Non-Negative Matrix Factorization. IEEE Trans. Pattern Anal. Mach. Intell. 41(1): 246-259 (2019) - [j29]Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao:
Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain. IEEE Trans. Pattern Anal. Mach. Intell. 41(4): 1013-1026 (2019) - [j28]Cheng Deng, Erkun Yang, Tongliang Liu, Jie Li, Wei Liu, Dacheng Tao:
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. IEEE Trans. Image Process. 28(8): 4032-4044 (2019) - [j27]Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongying Meng, Asoke K. Nandi:
Adaptive Morphological Reconstruction for Seeded Image Segmentation. IEEE Trans. Image Process. 28(11): 5510-5523 (2019) - [j26]Xinmei Tian, Ya Li, Tongliang Liu, Xinchao Wang, Dacheng Tao:
Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space. IEEE Trans. Neural Networks Learn. Syst. 30(6): 1818-1830 (2019) - [j25]Chen Gong, Tongliang Liu, Jian Yang, Dacheng Tao:
Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning. IEEE Trans. Neural Networks Learn. Syst. 30(11): 3471-3483 (2019) - [c25]Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, Dacheng Tao:
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. CVPR 2019: 2946-2955 - [c24]Yihang Lou, Ling-Yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen Gao:
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm. ICME 2019: 19-24 - [c23]Chuang Zhang, Dexin Ren, Tongliang Liu, Jian Yang, Chen Gong:
Positive and Unlabeled Learning with Label Disambiguation. IJCAI 2019: 4250-4256 - [c22]Yasutaka Furusho, Tongliang Liu, Kazushi Ikeda:
Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer. INNSBDDL 2019: 349-358 - [c21]Fengxiang He, Tongliang Liu, Dacheng Tao:
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. NeurIPS 2019: 1141-1150 - [c20]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS 2019: 6835-6846 - [i36]Yanwu Xu, Mingming Gong, Tongliang Liu, Kayhan Batmanghelich, Chaohui Wang:
Robust Angular Local Descriptor Learning. CoRR abs/1901.07076 (2019) - [i35]Fengxiang He, Tongliang Liu, Dacheng Tao:
Why ResNet Works? Residuals Generalize. CoRR abs/1904.01367 (2019) - [i34]Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich:
Generative-Discriminative Complementary Learning. CoRR abs/1904.01612 (2019) - [i33]Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao:
On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning. CoRR abs/1904.01747 (2019) - [i32]Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, Dacheng Tao:
A Regularization Approach for Instance-Based Superset Label Learning. CoRR abs/1904.02832 (2019) - [i31]Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan:
Supervised Discrete Hashing with Relaxation. CoRR abs/1904.03549 (2019) - [i30]Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan:
Fast Supervised Discrete Hashing. CoRR abs/1904.03556 (2019) - [i29]Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu:
Decomposition-Based Transfer Distance Metric Learning for Image Classification. CoRR abs/1904.03846 (2019) - [i28]Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu:
Multi-View Matrix Completion for Multi-Label Image Classification. CoRR abs/1904.03901 (2019) - [i27]Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongying Meng, Asoke K. Nandi:
Adaptive Morphological Reconstruction for Seeded Image Segmentation. CoRR abs/1904.03973 (2019) - [i26]Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao:
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. CoRR abs/1904.04061 (2019) - [i25]Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, Dacheng Tao:
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs. CoRR abs/1904.06505 (2019) - [i24]Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, Dacheng Tao:
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. CoRR abs/1905.03465 (2019) - [i23]Kui Jia, Shuai Li, Yuxin Wen, Tongliang Liu, Dacheng Tao:
Orthogonal Deep Neural Networks. CoRR abs/1905.05929 (2019) - [i22]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR abs/1906.00189 (2019) - [i21]Naiyang Guan, Tongliang Liu, Yangmuzi Zhang, Dacheng Tao, Larry S. Davis:
Truncated Cauchy Non-negative Matrix Factorization. CoRR abs/1906.00495 (2019) - [i20]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao:
A Quantum-inspired Algorithm for General Minimum Conical Hull Problems. CoRR abs/1907.06814 (2019) - [i19]Yihang Lou, Ling-Yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen Gao:
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm. CoRR abs/1907.13368 (2019) - [i18]Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? CoRR abs/1911.08696 (2019) - [i17]Xu Shen, Xinmei Tian, Tongliang Liu, Fang Xu, Dacheng Tao:
Continuous Dropout. CoRR abs/1911.12675 (2019) - [i16]Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao:
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. CoRR abs/1912.07010 (2019) - 2018
- [j24]Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan:
Fast Supervised Discrete Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 40(2): 490-496 (2018) - [j23]Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, Dacheng Tao:
A Regularization Approach for Instance-Based Superset Label Learning. IEEE Trans. Cybern. 48(3): 967-978 (2018) - [j22]Kede Ma, Huan Fu, Tongliang Liu, Zhou Wang, Dacheng Tao:
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks. IEEE Trans. Image Process. 27(10): 5155-5166 (2018) - [j21]Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan:
Supervised Discrete Hashing With Relaxation. IEEE Trans. Neural Networks Learn. Syst. 29(3): 608-617 (2018) - [j20]Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao:
On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning. IEEE Trans. Neural Networks Learn. Syst. 29(5): 1975-1985 (2018) - [j19]Ruxin Wang, Tongliang Liu, Dacheng Tao:
Multiclass Learning With Partially Corrupted Labels. IEEE Trans. Neural Networks Learn. Syst. 29(6): 2568-2580 (2018) - [j18]Xu Shen, Xinmei Tian, Tongliang Liu, Fang Xu, Dacheng Tao:
Continuous Dropout. IEEE Trans. Neural Networks Learn. Syst. 29(9): 3926-3937 (2018) - [c19]Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao:
Domain Generalization via Conditional Invariant Representations. AAAI 2018: 3579-3587 - [c18]Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, Jubo Zhu:
Reliable Multi-View Clustering. AAAI 2018: 4123-4130 - [c17]Yanwu Xu, Mingming Gong, Tongliang Liu, Kayhan Batmanghelich, Chaohui Wang:
Robust Angular Local Descriptor Learning. ACCV (5) 2018: 420-435 - [c16]Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, Dacheng Tao:
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. CVPR 2018: 4480-4489 - [c15]Xiyu Yu, Tongliang Liu, Mingming Gong, Dacheng Tao:
Learning with Biased Complementary Labels. ECCV (1) 2018: 69-85 - [c14]Baosheng Yu, Tongliang Liu, Mingming Gong, Changxing Ding, Dacheng Tao:
Correcting the Triplet Selection Bias for Triplet Loss. ECCV (6) 2018: 71-86 - [c13]Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao:
Deep Domain Generalization via Conditional Invariant Adversarial Networks. ECCV (15) 2018: 647-663 - [c12]Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, Dacheng Tao:
Semantic Structure-based Unsupervised Deep Hashing. IJCAI 2018: 1064-1070 - [c11]Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao:
Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization. IJCAI 2018: 2093-2099 - [c10]Yong Luo, Tongliang Liu, Yonggang Wen, Dacheng Tao:
Online Heterogeneous Transfer Metric Learning. IJCAI 2018: 2525-2531 - [i15]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier. CoRR abs/1802.03688 (2018) - [i14]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
An Information-Theoretic View for Deep Learning. CoRR abs/1804.09060 (2018) - [i13]Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao:
Domain Generalization via Conditional Invariant Representation. CoRR abs/1807.08479 (2018) - [i12]Fengxiang He, Tongliang Liu, Geoffrey I. Webb, Dacheng Tao:
Instance-Dependent PU Learning by Bayesian Optimal Relabeling. CoRR abs/1808.02180 (2018) - [i11]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao:
Implementable Quantum Classifier for Nonlinear Data. CoRR abs/1809.06056 (2018) - [i10]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao:
The Expressive Power of Parameterized Quantum Circuits. CoRR abs/1810.11922 (2018) - [i9]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
An Optimal Transport View on Generalization. CoRR abs/1811.03270 (2018) - 2017
- [j17]Tongliang Liu, Dacheng Tao, Mingli Song, Stephen J. Maybank:
Algorithm-Dependent Generalization Bounds for Multi-Task Learning. IEEE Trans. Pattern Anal. Mach. Intell. 39(2): 227-241 (2017) - [j16]Yuxiang Zhang, Bo Du, Liangpei Zhang, Tongliang Liu:
Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. IEEE Trans. Geosci. Remote. Sens. 55(2): 894-906 (2017) - [j15]Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu, Dacheng Tao:
Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification. IEEE Trans. Image Process. 26(1): 452-463 (2017) - [j14]Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, Dacheng Tao:
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs. IEEE Trans. Image Process. 26(8): 3951-3964 (2017) - [j13]Hongfu Liu, Junjie Wu, Tongliang Liu, Dacheng Tao, Yun Fu:
Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence. IEEE Trans. Knowl. Data Eng. 29(5): 1129-1143 (2017) - [j12]Tongliang Liu, Mingming Gong, Dacheng Tao:
Large-Cone Nonnegative Matrix Factorization. IEEE Trans. Neural Networks Learn. Syst. 28(9): 2129-2142 (2017) - [c9]Xiyu Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao:
On Compressing Deep Models by Low Rank and Sparse Decomposition. CVPR 2017: 67-76 - [c8]Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao:
Algorithmic Stability and Hypothesis Complexity. ICML 2017: 2159-2167 - [c7]Tongliang Liu, Qiang Yang, Dacheng Tao:
Understanding How Feature Structure Transfers in Transfer Learning. IJCAI 2017: 2365-2371 - [c6]Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao:
General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer. IJCAI 2017: 2450-2456 - [i8]Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao:
Algorithmic stability and hypothesis complexity. CoRR abs/1702.08712 (2017) - [i7]Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao:
Learning with Bounded Instance- and Label-dependent Label Noise. CoRR abs/1709.03768 (2017) - [i6]Xiyu Yu, Tongliang Liu, Mingming Gong, Dacheng Tao:
Learning with Biased Complementary Labels. CoRR abs/1711.09535 (2017) - 2016
- [b1]Tongliang Liu:
The complexity of algorithmic hypothesis class. University of Technology Sydney, Australia, 2016 - [j11]Tongliang Liu, Dacheng Tao, Dong Xu:
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes. Neural Comput. 28(10): 2213-2249 (2016) - [j10]Tongliang Liu, Dacheng Tao:
Classification with Noisy Labels by Importance Reweighting. IEEE Trans. Pattern Anal. Mach. Intell. 38(3): 447-461 (2016) - [j9]Jie Gui, Tongliang Liu, Dacheng Tao, Zhenan Sun, Tieniu Tan:
Representative Vector Machines: A Unified Framework for Classical Classifiers. IEEE Trans. Cybern. 46(8): 1877-1888 (2016) - [j8]Chang Xu, Tongliang Liu, Dacheng Tao, Chao Xu:
Local Rademacher Complexity for Multi-Label Learning. IEEE Trans. Image Process. 25(3): 1495-1507 (2016) - [j7]Hao Xiong, Tongliang Liu, Dacheng Tao, Heng Tao Shen:
Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing. IEEE Trans. Image Process. 25(8): 3626-3637 (2016) - [j6]Xiaoyan Li, Tongliang Liu, Jiankang Deng, Dacheng Tao:
Video Face Editing Using Temporal-Spatial-Smooth Warping. ACM Trans. Intell. Syst. Technol. 7(3): 32:1-32:28 (2016) - [j5]Tongliang Liu, Dacheng Tao:
On the Performance of Manhattan Nonnegative Matrix Factorization. IEEE Trans. Neural Networks Learn. Syst. 27(9): 1851-1863 (2016) - [c5]Hao Xiong, Tongliang Liu, Dacheng Tao:
Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. AAAI 2016: 3641-3647 - [c4]Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf:
Domain Adaptation with Conditional Transferable Components. ICML 2016: 2839-2848 - [i5]Tongliang Liu, Dacheng Tao, Dong Xu:
Dimensionality-Dependent Generalization Bounds for $k$-Dimensional Coding Schemes. CoRR abs/1601.00238 (2016) - [i4]Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu, Dacheng Tao:
Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification. CoRR abs/1603.01096 (2016) - [i3]Kede Ma, Huan Fu, Tongliang Liu, Zhou Wang, Dacheng Tao:
Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks. CoRR abs/1612.01227 (2016) - 2015
- [j4]Yanan Lu, Fengying Xie, Tongliang Liu, Zhiguo Jiang, Dacheng Tao:
No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model. IEEE Signal Process. Lett. 22(10): 1811-1815 (2015) - [j3]Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu:
Multiview Matrix Completion for Multilabel Image Classification. IEEE Trans. Image Process. 24(8): 2355-2368 (2015) - [j2]Chen Gong, Tongliang Liu, Dacheng Tao, Keren Fu, Enmei Tu, Jie Yang:
Deformed Graph Laplacian for Semisupervised Learning. IEEE Trans. Neural Networks Learn. Syst. 26(10): 2261-2274 (2015) - [c3]Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao:
Multi-Task Model and Feature Joint Learning. IJCAI 2015: 3643-3649 - [c2]Hongfu Liu, Tongliang Liu, Junjie Wu, Dacheng Tao, Yun Fu:
Spectral Ensemble Clustering. KDD 2015: 715-724 - 2014
- [j1]Yong Luo, Tongliang Liu, Dacheng Tao, Chao Xu:
Decomposition-Based Transfer Distance Metric Learning for Image Classification. IEEE Trans. Image Process. 23(9): 3789-3801 (2014) - [c1]Ming Shao, Sheng Li, Tongliang Liu, Dacheng Tao, Thomas S. Huang, Yun Fu:
Learning relative features through adaptive pooling for image classification. ICME 2014: 1-6 - [i2]Chang Xu, Tongliang Liu, Dacheng Tao, Chao Xu:
Local Rademacher Complexity for Multi-label Learning. CoRR abs/1410.6990 (2014) - [i1]Tongliang Liu, Dacheng Tao:
Classification with Noisy Labels by Importance Reweighting. CoRR abs/1411.7718 (2014)
Coauthor Index
[j94] [i221] [i220] [i216] [j92] [c178] [c159] [c153] [c151] [i206] [i204] [i185] [i182] [i176] [i174] [i172] [j67] [c138] [c133] [c131] [c128] [c122] [c119] [c107] [i159] [i141] [c99] [c94] [c93] [c87] [c86] [c84] [c79] [c76] [c74] [c66] [c60] [i114] [i105] [i104] [i103] [i97] [i96] [i92] [i91] [j41] [c58] [c46] [c44] [c41] [i79] [i74] [i73] [i68] [i61] [i60] [i59] [i55] [i53] [c35] [c34] [c32] [c31] [c28] [c27] [c26] [i51] [i50] [i47] [i46] [i45] [i36] [i34] [c19] [c17] [c16] [c15] [c14] [c13] [i13] [j12] [i6] [c4]
[j96] [j94] [j93] [i215] [i213] [i211] [j88] [j87] [j86] [j85] [j81] [j79] [c179] [c177] [c173] [c172] [c171] [c168] [c167] [c166] [c165] [c162] [c161] [c160] [c158] [c157] [c154] [c151] [c150] [c149] [c147] [c145] [i206] [i205] [i202] [i201] [i200] [i199] [i198] [i197] [i196] [i191] [i189] [i188] [i186] [i184] [i179] [i178] [i177] [i171] [j74] [j71] [j68] [j66] [c143] [c140] [c138] [c131] [c129] [c126] [c125] [c124] [c122] [c121] [c120] [c119] [c116] [c115] [c112] [c110] [c109] [c104] [c103] [c102] [i168] [i167] [i166] [i161] [i156] [i153] [i152] [i148] [i147] [i142] [i141] [i133] [i129] [i128] [i127] [i125] [i124] [i123] [i122] [i121] [i120] [j60] [j58] [j49] [c100] [c99] [c92] [c90] [c89] [c87] [c86] [c85] [c84] [c83] [c80] [c79] [c78] [c77] [c76] [c75] [c70] [c69] [c68] [c67] [c63] [c62] [c61] [c60] [i114] [i113] [i112] [i106] [i104] [i103] [i102] [i101] [i99] [i97] [i96] [i95] [i93] [i92] [i90] [i89] [c59] [c58] [c51] [c50] [c49] [c48] [c47] [c46] [c45] [c41] [c40] [c39] [c38] [i86] [i85] [i84] [i83] [i82] [i79] [i76] [i75] [i74] [i73] [i71] [i70] [i69] [i68] [i67] [i66] [i65] [i64] [i63] [i59] [i58] [c28] [c27] [i52] [i51] [i50] [i47] [i46] [i45] [i41] [i40] [i39] [c20] [i22] [i18]
[j93] [i173] [j71] [c122] [c120] [c102] [i163] [i148] [i147] [i141] [i125] [j60] [j49] [c100] [c92] [c89] [c88] [c87] [c86] [c85] [c84] [c83] [c81] [c80] [c69] [i101] [i89] [c59] [c50] [c49] [c48] [c47] [c46] [c41] [c39] [c38] [i86] [i85] [i84] [i83] [i82] [i76] [i75] [i74] [i73] [i72] [i71] [i68] [i64] [i63] [i59] [i57] [c28] [c27] [i52] [i51] [i50] [i47] [i46] [i45] [i41] [i40] [c20] [i22] [i18]
[j95] [j93] [i216] [j92] [j83] [j77] [c146] [j72] [j63] [c144] [c142] [c127] [c114] [c113] [i165] [i155] [i144] [i140] [i137] [i134] [j59] [j58] [j57] [j54] [j52] [j46] [c86] [i107] [i100] [i94] [i91] [i88] [j44] [j43] [j42] [j37] [j34] [j33] [j32] [c36] [c33] [c32] [c31] [c28] [c26] [i51] [i49] [i48] [i45] [i43] [i42] [j30] [j29] [j28] [j26] [j25] [c25] [c21] [i35] [i33] [i32] [i31] [i30] [i29] [i28] [i26] [i25] [i24] [i23] [i21] [i20] [i17] [i16] [j24] [j23] [j22] [j21] [j20] [j19] [j18] [c19] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [j17] [j15] [j14] [j13] [j12] [c9] [c8] [c7] [c6] [i8] [i7] [i6] [j11] [j10] [j9] [j8] [j7] [j6] [j5] [c5] [c4] [i5] [i4] [i3] [j4] [j3] [j2] [c3] [c2] [j1] [c1] [i2] [i1]
[j88] [j85] [j84] [j80] [c185] [c183] [c169] [c162] [c157] [c156] [c145] [i204] [i200] [i198] [i197] [i192] [i180] [i176] [j74] [j70] [c143] [c140] [c138] [c136] [c131] [c129] [c125] [c103] [i169] [i164] [i161] [i154] [i148] [i133] [i131] [i126] [i120] [i119] [i116] [i115] [j47] [j45] [c98] [c87] [c74] [c65] [c64] [c61] [i111] [i106] [i96] [c51] [c46] [i74] [i73] [i61] [c28] [i50] [i46] [i45] [i39] [c20] [i22]
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