Reviews: Deep Active Learning with a Neural Architecture Search

This paper proposes a strategy for an efficient deep network architecture search (here for image classification, but a the general idea would apply for other tasks as well). The proposed strategy is will motivated and involves a data sampling stage at each step. Here, an active querying strategy can be employed and the authors evaluate their strategy with three different active sampling strategies. They show that their strategy improves over active learning (with the same active query strategies) with a fixed architecture. However, the reviewers have rightly pointed out that a comparison with other architecture search strategies would also have been in place.