Review for NeurIPS paper: The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification

Summary and Contributions: This paper aims to alleviate the dilemma of triplet loss when facing hard negative samples. The "dilemma" mentioned in this paper means that the similarity between the anchor and positive sample is useful for better representation, but the similarity between the anchor and negative sample should be repelled, so processing the similarity of anchor, positive sample and hard negative samples is a dilemma problem in triplet loss. To solve this problem, an Element-weighted TriHard Loss function is designed in this paper. The main idea is to weight the feature vectors of anchor and negative sample before calculating their distance to find discriminative elements of their feature vectors. Meanwhile, three mitigation strategies of TriHard loss dilemma are discussed in this paper.