Improving Diversity with Adversarially Learned Transformations for Domain Generalization. (arXiv:2206.07736v1 [cs.LG])

Web: http://arxiv.org/abs/2206.07736

To be successful in single source domain generalization, maximizing diversity
of synthesized domains has emerged as one of the most effective strategies.
Many of the recent successes have come from methods that pre-specify the types
of diversity that a model is exposed to during training, so that it can
ultimately generalize well to new domains. However, na\"ive diversity based
augmentations do not work effectively for domain generalization either because
they cannot model large domain shift, or because the span of …

arxiv diversity lg