Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …

Abstract Unsupervised image Anomaly Detection (UAD) aims to learn robust and
discriminative representations of normal samples. While separate solutions per class endow …

Endeavors have been recently made to leverage the vision transformer (ViT) for the
challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross …

Through prompting, large-scale pre-trained models have become more expressive and
powerful, gaining significant attention in recent years. Though these big models have zero …

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 …

Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability
to support heterogeneous models and data. To reduce the high communication cost of …

Abstract Domain adaptive object detection aims to adapt detection models to domains
where annotated data is unavailable. Existing methods have been proposed to address the …

Abstract Existing Source-Free Domain Adaptation (SFDA) methods typically adopt the
feature distribution alignment paradigm via mining auxiliary information (eg., pseudo …