Reviews: Consistency-based Semi-supervised Learning for Object detection

The paper presents a semi-supervised approach for object detection that extends the consistency regularization used for image classification [14] for object detection. Concretely, it proposes using consistency losses for both classification and localization, as well as a background elimination technique that alleviates the class imbalance inherent to object detection. They evaluate their approach with two types of detectors (single and two-stage) on PASCAL VOT 2007 with unlabeled data from VOT2012 and COCO. Pros: The approach is novel, as far as I know no previous work addresses semi-supervised learning with consistency regularization for object detection. The use of JS divergence over L2 distance is justified and shown experimentally.