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 …

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …

Active domain adaptation (ADA) aims to improve the model adaptation performance by
incorporating the active learning (AL) techniques to label a maximally-informative subset of …

This work focuses on a practical knowledge transfer task defined as Source-Free
Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and …

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an
unlabelled target domain without accessing the source dataset, making it applicable in a …

Video domain generalization aims to learn generalizable video classification models for
unseen target domains by training in a source domain. A critical challenge of video domain …

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 …

The evolution of autonomous driving has made remarkable advancements in recent years,
evolving into a tangible reality. However, a human-centric large-scale adoption hinges on …

Abstract Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence
of both covariate and label shifts. Recently Source-free Universal Domain Adaptation (SF …

Abstract Knowledge distillation aims to learn a lightweight student network from a pre-
trained teacher network. In practice, existing knowledge distillation methods are usually …