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 …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

This paper focuses on the unsupervised domain adaptation of transferring the knowledge
from the source domain to the target domain in the context of semantic segmentation …

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Unsupervised domain adaptation (UDA) aims to improve the classification performance on
an unlabeled target domain by leveraging information from a fully labeled source domain …

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a
target domain, given labeled data on a source domain whose distribution shifts from the …

There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain
configurations, including closed-set and partial-set DA, as well as multi-source and multi …