Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

Test-time adaptation is a special setting of unsupervised domain adaptation where a trained
model on the source domain has to adapt to the target domain without accessing source …

In recent years, data-driven methods have been widely used in rolling bearing fault
diagnosis with great success, which mainly relies on the same data distribution and massive …

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based
methods show promising performance, but two challenges remain: how to explicitly model …

Abstract Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we investigate Source-free …

The key to address clothes-changing person re-identification (re-id) is to extract clothes-
irrelevant features, eg, face, hairstyle, body shape, and gait. Most current works mainly focus …

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 …

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