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 …

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

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

Abstract Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network (CNN)-based approaches for semantic segmentation heavily …

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …

Training state-of-the-art vision models has become prohibitively expensive for researchers
and practitioners. For the sake of accessibility and resource reuse, it is important to focus on …

Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich but
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …

We study a practical domain adaptation task, called source-free unsupervised domain
adaptation (UDA) problem, in which we cannot access source domain data due to data …

Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks
when learning new ones. In this paper we focus on class incremental continual learning in …

Conventional machine learning algorithms suffer the problem that the model trained on
existing data fails to generalize well to the data sampled from other distributions. To tackle …