Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Albeit the notable performance on in-domain test points, it is non-trivial for deep neural
networks to attain satisfactory accuracy when deploying in the open world, where novel …

Abstract Domain generalization (DG) is proposed to deal with the issue of domain shift,
which occurs when statistical differences exist between source and target domains …

The generalization capability of machine learning systems degenerates notably when the
test distribution drifts from the training distribution. Recently, Domain Generalization (DG) …

Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-
labeled source domain to an unlabeled target domain. Large-data pre-trained networks are …

With recent advancements in artificial intelligence, its applications can be seen in every
aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous …

Universal domain adaptation (UniDA) is a practical but challenging problem, in which
information about the relation between the source and the target domains is not given for …

Unsupervised domain adaptation (UDA) has proven to be very effective in transferring
knowledge obtained from a source domain with labeled data to a target domain with …

Abstract We delve into Open Domain Generalization (ODG) marked by domain and category
shifts between training's labeled source and testing's unlabeled target domains. Existing …

Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-
labeled source domain to a related but unlabeled target domain. While the source model is …