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

Understand

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.