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 …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …

Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …

Unsupervised domain adaptation (UDA) aims to improve the classification performance on
an unlabeled target domain by leveraging information from a fully labeled source domain …

Applying a trained model on a new scenario may suffer from domain shift. Unsupervised
domain adaptation (UDA) has been proven to be an effective approach to solve the problem …

Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a
source domain to unlabelled data in a target domain without access to the source-domain …

Egocentric 3D human pose estimation using a single fisheye camera has become popular
recently as it allows capturing a wide range of daily activities in unconstrained environments …

Unsupervised domain adaptation (UDA) aims to learn models for a target domain of
unlabeled data by transferring knowledge from a labeled source domain. In the traditional …

Unsupervised domain adaptation (UDA) aims to leverage a sufficiently labeled source
domain to classify or represent the fully unlabeled target domain with a different distribution …