Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation. (arXiv:2206.11180v1 [cs.CV])

Web: http://arxiv.org/abs/2206.11180

It is common in computer vision to be confronted with domain shift: images
which have the same class but different acquisition conditions. In domain
adaptation (DA), one wants to classify unlabeled target images using source
labeled images. Unfortunately, deep neural networks trained on a source
training set perform poorly on target images which do not belong to the
training domain. One strategy to improve these performances is to align the
source and target image distributions in an embedded space using …

arxiv cv domain adaptation loss regularization transport