… We propose a variational Bayesian framework for enhancing few-shot learning performance…
We propose a variational Bayesian framework for fewshot learning. Different from the …

… Using auxiliary information for few-shot learning … few-shot learning is learned. Analogous
to the relation between ZSL and GZSL, we extend few-shot to the generalized few-shot learning …

… Therefore, few-shot learning [8] (FSL) is proposed to study how to learn the key information
from the insufficient labeled novel samples on novel few-shot classification (FSC) tasks …

… learners to improve adaptive capacity [28][8]. Considering that the critical problem of few-shot
learning … space within a Gaussian distribution by variational inference; 2) We propose a …

… /inter-class-structure-based variational few-shot learning (OICS-VFSL) model to overcome
a specific out-of-distribution problem in imbalanced learning, and to improve the microservice-…

… -domain few-shot learning by a variational … variational memory for cross-domain few-shot
learning, and to better deploy the hierarchical memory, we introduce a hierarchical variational …

… To realize such an idea, we present a novel deep generative approach named semi-identical
twins variational autoencoder (STVAE) for few-shot learning from the perspective of data …

… , we propose a method for fewshot learning with fewer tasks, … learning variational feature
hierarchies by the variational … variants in few-task meta-learning. Our MetaModulation and its …

… As such, few shot learning across domain requires stronger generalized … learning and
variational inference, we propose variational hyperparameter optimization to address few shot …

… In our paper, we intend to generate the deep features for the few-shot categories and then
consider few-shot learning task as an ordinary classification issue for solution. Variational Au…