Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Scalable systems for automated driving have to reliably cope with an open-world setting.
This means, the perception systems are exposed to drastic domain shifts, like changes in …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

Abstract Domain generalization (DG) aims to generalize a model trained on multiple source
(ie, training) domains to a distributionally different target (ie, test) domain. In contrast to the …

Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in
the unlabeled target domain using the pre-trained source model, not the source data …

Open-world classification systems should discern out-of-distribution (OOD) data whose
labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD …

Unsupervised domain adaptation has attracted appealing academic attentions by
transferring knowledge from labeled source domain to unlabeled target domain. However …

Recently, deep transfer learning-based intelligent machine diagnosis has been well
investigated, and the source and the target domain are commonly assumed to share the …

Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has
emerged as a crucial approach for domain-adaptive 3D object detection. While effective …

Abstract Open Set Domain Adaptation (OSDA) transfers the model from a label-rich domain
to a label-free one containing novel-class samples. Existing OSDA works overlook abundant …