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 …

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …

Transfer learning can effectively solve the target task identification problem with the
prerequisite of sharing all user data and target data, and has become one of the most …

Time series has wide applications in the real world and is known to be difficult to forecast.
Since its statistical properties change over time, its distribution also changes temporally …

In this paper, we investigate a challenging unsupervised domain adaptation setting---
unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data …

Generalization capability to unseen domains is crucial for machine learning models when
deploying to real-world conditions. We investigate the challenging problem of domain …

As a new classification platform, deep learning has recently received increasing attention
from researchers and has been successfully applied to many domains. In some domains …

In this paper, we tackle the problem of domain generalization: how to learn a generalized
feature representation for an “unseen” target domain by taking the advantage of multiple …

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 …

Domain adaptation studies learning algorithms that generalize across source domains and
target domains that exhibit different distributions. Recent studies reveal that deep neural …