Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into
transfer learning (TL), has achieved excellent success in computer vision, text classification …

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 …

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 …

Abstract Domain adversarial training has been ubiquitous for achieving invariant
representations and is used widely for various domain adaptation tasks. In recent times …

Abstract Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source
domain to solve tasks on a related unlabeled target domain. It is a challenging problem …

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

Although deep learning models have shown impressive performance on supervised
learning tasks, they often struggle to generalize well when the training (source) and test …

Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled
source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge …

Abstract Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source
domain to tackle the learning tasks on the unlabeled target domain. It can be more …