Due to privacy concerns and data transmission issues, Source-free Unsupervised Domain
Adaptation (SFDA) has gained popularity. It exploits pre-trained source models, rather than …

Adversarial learning has proven to be an effective method for capturing transferable features
for unsupervised domain adaptation. However, some existing conditional adversarial …

In unsupervised domain adaptation (UDA), knowledge is transferred from label-rich source
domains to relevant but unlabeled target domains. Current most popular state-of-the-art …

We address the source-free domain adaptation (SFDA) problem, where only the source
model is available during adaptation to the target domain. We consider two settings: the …

Lung cancer is the leading cause of cancer death worldwide. The best solution for lung
cancer is to diagnose the pulmonary nodules in the early stage, which is usually …

Domain Generalization (DG) aims to improve the generalization ability of models trained on
a specific group of source domains, enabling them to perform well on new, unseen target …

Domain generalization refers to the challenge of training a model from various source
domains that can generalize well to unseen target domains. Contrastive learning is a …

Graph similarity estimation is a challenging task due to the complex graph structure. Though
important and well-studied, three key aspects are yet to be fully handled in a unified …

Unsupervised domain adaptation (UDA) trains models using labeled data from a specific
source domain and then transferring the knowledge to certain target domains that have few …

Although great success has been achieved in various computer vision tasks, deep neural
networks (DNNs) suffer dramatic performance degradation when evaluated on out-of …