Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

We propose a simple but effective source-free domain adaptation (SFDA) method. Treating
SFDA as an unsupervised clustering problem and following the intuition that local neighbors …

Abstract Universal Domain Adaptation (UNDA) aims to handle both domain-shift and
category-shift between two datasets, where the main challenge is to transfer knowledge …

Deep neural networks (DNNs) often perform poorly in the presence of domain shift and
category shift. How to upcycle DNNs and adapt them to the target task remains an important …

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to
transfer the knowledge from source to target under unaligned label space. The main …

Open-set domain adaptation is a developing and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

Abstract Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source
domain to a target domain without any constraints on label sets. Since both domains may …

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS), where
the label distribution can change arbitrarily and a new class may arrive during deployment …

Abstract Open Set Domain Adaptation (OSDA) transfers the model from a label-rich domain
to a label-free one containing novel-class samples. Existing OSDA works overlook abundant …

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-
rich source domain to a label-scarce target domain without any constraints on the label …