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In this paper, we are proposing a unified and principled method for both thequerying and training processes in deep batch active learning. We are providingtheoretical insights from the intuition of modeling the interactive procedurein active learning as distribution matching, by adopting the Wassersteindistance. As a consequence, we derived a new training loss from the theoreticalanalysis, which is decomposed into optimizing deep neural network parametersand batch query selection through alternative optimization. In addition, theloss for training a deep neural network is naturally formulated as a min-maxoptimization problem through leveraging the unlabeled data information.Moreover, the proposed principles also indicate an explicituncertainty-diversity trade-off in the query batch selection. Finally, weevaluate our proposed method on different benchmarks, consistently showingbetter empirical performances and a better time-efficient query strategycompared to the baselines.

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