  Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable
features from a well-labeled source domain to a related unlabeled target
domain, has been widely progressed. Nevertheless, as one of the mainstream,
existing adversarial-based methods neglect to filter the irrelevant semantic
knowledge, hindering adaptation performance improvement. Besides, they require
an additional domain discriminator that strives extractor to generate confused
representations, but discrete designing may cause model collapse. To tackle the
above issues, we propose Crucial Semantic Classifier-based Adversarial Learning
(CSCAL), which pays more attention to crucial semantic knowledge transferring
and leverages the classifier to implicitly play the role of domain
discriminator without extra network designing. Specifically, in
intra-class-wise alignment, a Paired-Level Discrepancy (PLD) is designed to
transfer crucial semantic knowledge. Additionally, based on classifier
predictions, a Nuclear Norm-based Discrepancy (NND) is formed that considers
inter-class-wise information and improves the adaptation performance. Moreover,
CSCAL can be effortlessly merged into different UDA methods as a regularizer
and dramatically promote their performance.
