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 …

We consider unsupervised domain adaptation (UDA), where labeled data from a source
domain (eg, photos) and unlabeled data from a target domain (eg, sketches) are used to …

Machine learning systems deployed in the wild are often trained on a source distribution but
deployed on a different target distribution. Unlabeled data can be a powerful point of …

Abstract Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant predictive …

Self-training and contrastive learning have emerged as leading techniques for incorporating
unlabeled data, both under distribution shift (unsupervised domain adaptation) and when it …

Abstract Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer
between two datasets with domain-shift as well as category-shift. The goal is to categorize …

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift
between the source and target domains. Prior DA works show that pretext tasks could be …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain
distributions, with a small number of target labels available, achieving better classification …

Existing domain adaptation methods usually perform explicit representation alignment by
simultaneously accessing the source data and target data. However, the source data are not …