NeurIPS 2020

One-bit Supervision for Image Classification

Meta Review

The paper proposes a new paradigm for image annotation called one-bit supervision based on questioning whether a random image belongs to a predicted category or not. Under the assumption that annotating an image with K categories is as expensive as log K annotations of the form of one-bit supervision, the paper shows that multi-stage semi-supervised learning using one-bit supervision is more effective than standard semi-supervised learning under the same annotation costs. The setup is interesting and convincing as the first step, but as the reviewers noted, the clarity of exposition and claims can improve. Also, it is worth elaborating whether you use softmax cross-entropy loss as mentioned in L112 or L2 loss in Eq. (1).