Dissecting the impact of different loss functions with gradient surgery. (arXiv:2201.11307v1 [cs.CV])

Web: http://arxiv.org/abs/2201.11307

Pair-wise loss is an approach to metric learning that learns a semantic
embedding by optimizing a loss function that encourages images from the same
semantic class to be mapped closer than images from different classes. The
literature reports a large and growing set of variations of the pair-wise loss
strategies. Here we decompose the gradient of these loss functions into
components that relate to how they push the relative feature positions of the
anchor-positive and anchor-negative pairs. This decomposition allows …

arxiv cv gradient impact