Deep Active Learning with a Neural Architecture Search

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Yonatan Geifman, Ran El-Yaniv


<p>We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures.</p>