Poster
Deep Active Learning with a Neural Architecture Search
Yonatan Geifman · Ran El-Yaniv
East Exhibition Hall B + C #136
Keywords: [ Active Learning ] [ Algorithms ] [ CNN Architectures ] [ Algorithms -> AutoML; Deep Learning ]
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.
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