Neural Bregman Divergences for Distance Learning. (arXiv:2206.04763v1 [cs.LG])

Many metric learning tasks, such as triplet learning, nearest neighbor
retrieval, and visualization, are treated primarily as embedding tasks where
the ultimate metric is some variant of the Euclidean distance (e.g., cosine or
Mahalanobis), and the algorithm must learn to embed points into the pre-chosen
space. The study of non-Euclidean geometries or appropriateness is often not
explored, which we believe is due to a lack of tools for learning non-Euclidean
measures of distance. Under the belief that the use of …

arxiv distance learning learning lg