Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …

Abstract Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time consuming …

Instance contrast for unsupervised representation learning has achieved great success in
recent years. In this work, we explore the idea of instance contrastive learning in …

Abstract Domain adaptation aims to transfer knowledge in the presence of the domain gap.
Existing domain adaptation methods rely on rich prior knowledge about the relationship …

In unsupervised domain adaptation, rich domain-specific characteristics bring great
challenge to learn domain-invariant representations. However, domain discrepancy is …

In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality
variations between RGB and IR images. The key solution is to learn aligned features to the …

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source
domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to …