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Pair-wise loss is an approach to metric learning that learns a semanticembedding by optimizing a loss function that encourages images from the samesemantic class to be mapped closer than images from different classes. Theliterature reports a large and growing set of variations of the pair-wise lossstrategies. Here we decompose the gradient of these loss functions intocomponents that relate to how they push the relative feature positions of theanchor-positive and anchor-negative pairs. This decomposition allows theunification of a large collection of current pair-wise loss functions.Additionally, explicitly constructing pair-wise gradient updates to separateout these effects gives insights into which have the biggest impact, and leadsto a simple algorithm that beats the state of the art for image retrieval onthe CAR, CUB and Stanford Online products datasets.

Dissecting the impact of different loss functions with gradient surgery