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 …

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source
domain to an unlabeled target domain. Existing UDA-based semantic segmentation …

Few-shot learning (FSL) aims at fast adaptation to novel classes with few training samples.
Among FSL methods, meta-learning and transfer learning-based methods are the most …

Adversarial domain adaptation has made remarkable in promoting feature transferability,
while recent work reveals that there exists an unexpected degradation of feature …

In this paper, we tackle the task of domain adaptation under noisy environments; this is a
practical and challenging problem in which the source domain is corrupted with noise in its …

Unsupervised domain adaptation aims to leverage knowledge from a labeled source
domain to learn an accurate model in an unlabeled target domain. However, many previous …

Few-shot action recognition classifies new actions with only few training samples, of which
the mainstream methods adopt class means to obtain prototypes as the representations of …

Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a
labeled source domain and adapts a trained model to an unlabeled target domain. To …

Deep learning is reaching state of the art in many applications. However, the generalization
capabilities of the learned networks are limited to the training or source domain. The …

Domain adaptation enables generalized learning in new environments by transferring
knowledge from label-rich source domains to label-scarce target domains. As a more …