Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging
visual learning tasks, which can be cast as a feature distribution matching problem. With the …

Abstract Unsupervised Domain Adaptation solves knowledge transfer along with the
coexistence of well-annotated source domain and unlabeled target instances. However, the …

Adversarial learning has achieved remarkable performances for unsupervised domain
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …

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 …

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …

Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies were …

The learning of the deep networks largely relies on the data with human-annotated labels. In
some label insufficient situations, the performance degrades on the decision boundary with …

Abstract Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned
from a well-labeled source domain to an unlabled target domain. Recently, adversarial …