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 …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Depth cues are known to be useful for visual perception. However, direct measurement of
depth is often impracticable. Fortunately, though, modern learning-based methods offer …

Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-
by-batch manner during inference. While label distributions often exhibit imbalances in real …

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned
model to other domains. The domain-invariant knowledge is transferred from the model …

Most unsupervised domain adaptation (UDA) methods assume that labeled source images
are available during model adaptation. However, this assumption is often infeasible owing to …

Unsupervised domain adaptation (UDA) aims at transferring knowledge from one labeled
source domain to a related but unlabeled target domain. Recently, active domain adaptation …

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model
using unlabeled target data and the knowledge of a well-trained source domain model. Most …

Considerable progress has been made in domain generalization (DG) which aims to learn a
generalizable model from multiple well-annotated source domains to unknown target …