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 …

Partial domain adaptation (PDA) assumes that target domain class label set is a subset of
that of source domain, while this problem setting is close to the actual scenario. At present …

Source-free domain adaptation (SFDA) tends to forget the source domain, suffering from
limitations in real-world scenarios. Recently, generalized source-free domain adaptation …

Recently, domain adaptation (DA)-based fault diagnosis approaches have been actively
studied in chemical processes to build a reliable fault diagnosis model for a new operating …

Source-free unsupervised domain adaptation (SFUDA) aims to conduct prediction on the
target domain by leveraging knowledge from the well-trained source model. Due to the …

Source-free domain adaptive object detection (source-free DAOD) seeks to adapt a detector
pre-trained on a source domain to an unlabeled target domain without requiring access to …