  Adversarial learning has achieved remarkable performances for unsupervised
domain adaptation (UDA). Existing adversarial UDA methods typically adopt an
additional discriminator to play the min-max game with a feature extractor.
However, most of these methods failed to effectively leverage the predicted
discriminative information, and thus cause mode collapse for generator. In this
work, we address this problem from a different perspective and design a simple
yet effective adversarial paradigm in the form of a discriminator-free
adversarial learning network (DALN), wherein the category classifier is reused
as a discriminator, which achieves explicit domain alignment and category
distinguishment through a unified objective, enabling the DALN to leverage the
predicted discriminative information for sufficient feature alignment.
Basically, we introduce a Nuclear-norm Wasserstein discrepancy (NWD) that has
definite guidance meaning for performing discrimination. Such NWD can be
coupled with the classifier to serve as a discriminator satisfying the
K-Lipschitz constraint without the requirements of additional weight clipping
or gradient penalty strategy. Without bells and whistles, DALN compares
favorably against the existing state-of-the-art (SOTA) methods on a variety of
public datasets. Moreover, as a plug-and-play technique, NWD can be directly
used as a generic regularizer to benefit existing UDA algorithms. Code is
available at https://github.com/xiaoachen98/DALN.
