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 …

Abstract Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread
attention due to their availability to transfer knowledge from multiple source domains to the …

Images can convey rich semantics and induce various emotions in viewers. Recently, with
the rapid advancement of emotional intelligence and the explosive growth of visual data …

The rise of deep neural networks has led to several breakthroughs for semantic
segmentation. In spite of this, a model trained on source domain often fails to work properly …

Abstract Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a
new target domain by actively selecting a limited number of target data to annotate. This …

Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain
generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers …

To alleviate the need for large-scale pixel-wise annotations, domain adaptation for semantic
segmentation trains segmentation models on synthetic data (source) with computer …

Transfer learning becomes an attractive technology to tackle a task from a target domain by
leveraging previously acquired knowledge from a similar domain (source domain). Many …

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on
condition that source and target data are identically distributed. In engineering practice …

For cross-domain pattern classification, the supervised information (ie, labeled patterns) in
the source domain is often employed to help classify the unlabeled target domain patterns …