Deep Active Learning with Adaptive Acquisition
Deep Active Learning with Adaptive Acquisition
Manuel Haussmann, Fred Hamprecht, Melih Kandemir
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2470-2476.
https://doi.org/10.24963/ijcai.2019/343
Model selection is treated as a standard performance boosting step in many machine learning applications. Once all other properties of a learning problem are fixed, the model is selected by grid search on a held-out validation set. This is strictly inapplicable to active learning. Within the standardized workflow, the acquisition function is chosen among available heuristics a priori, and its success is observed only after the labeling budget is already exhausted. More importantly, none of the earlier studies report a unique consistently successful acquisition heuristic to the extent to stand out as the unique best choice. We present a method to break this vicious circle by defining the acquisition function as a learning predictor and training it by reinforcement feedback collected from each labeling round. As active learning is a scarce data regime, we bootstrap from a well-known heuristic that filters the bulk of data points on which all heuristics would agree, and learn a policy to warp the top portion of this ranking in the most beneficial way for the character of a specific data distribution. Our system consists of a Bayesian neural net, the predictor, a bootstrap acquisition function, a probabilistic state definition, and another Bayesian policy network that can effectively incorporate this input distribution. We observe on three benchmark data sets that our method always manages to either invent a new superior acquisition function or to adapt itself to the a priori unknown best performing heuristic for each specific data set.
Keywords:
Machine Learning: Active Learning
Uncertainty in AI: Approximate Probabilistic Inference
Machine Learning: Probabilistic Machine Learning
Machine Learning: Deep Learning
Humans and AI: Human-AI Collaboration