Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

Reducing the feature distribution shift caused by the factor of visual-environment changes,
named visual-environment changes (VE-changes), is a hot issue in domain adaptation …

To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer
knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset …

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD
algorithms either rely on restricted assumptions or fail to exploit environment information in …

Source free domain adaptation (SFDA) aims to transfer a trained source model to the
unlabeled target domain without accessing the source data. However, the SFDA setting …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Abstract Conventional Domain Adaptation (DA) methods aim to learn domain-invariant
feature representations to improve the target adaptation performance. However, we motivate …