Adversarial learning has achieved remarkable performances for unsupervised domain
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …

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 …

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system
originally developed for continuous and non-invasive monitoring of brain function by …

Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled
target set, assuming that the source dataset is available with all labels. However, the …

Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …

Given labeled instances on a source domain and unlabeled ones on a target domain,
unsupervised domain adaptation aims to learn a task classifier that can well classify target …

Understanding and explaining deep learning models is an imperative task. Towards this, we
propose a method that obtains gradient-based certainty estimates that also provide visual …

Having a lot of labeled data is always a problem in machine learning issues. Even by
collecting lots of data hardly, shift in data distribution might emerge because of differences in …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

Domain adaptation methods reduce domain shift typically by learning domain-invariant
features. Most existing methods are built on distribution matching, eg, adversarial domain …