Deep Active Learning: Unified and Principled Method for Query and Training

Abstract

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. We are providing theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting the Wasserstein distance. As a consequence, we derived a new training loss from the theoretical analysis, which is decomposed into optimizing deep neural network parameters and batch query selection through alternative optimization. In addition, the loss for training a deep neural network is naturally formulated as a min-max optimization problem through leveraging the unlabeled data information. Moreover, the proposed principles also indicate an explicit uncertainty-diversity trade-off in the query batch selection. Finally, we evaluate our proposed method on different benchmarks, consistently showing better empirical performances and a better time-efficient query strategy compared to the baselines.

AISTATS 2020

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Discriminative Active Learning
• Deep Active Learning over the Long Tail
• Learning Algorithms for Active Learning
• Bayesian Generative Active Deep Learning
• Nuclear Discrepancy for Active Learning

Cited by in corpus (2)

• Discriminative Active Learning for Domain Adaptation
• Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning