  The recent advances in deep transfer learning reveal that adversarial
learning can be embedded into deep networks to learn more transferable features
to reduce the distribution discrepancy between two domains. Existing
adversarial domain adaptation methods either learn a single domain
discriminator to align the global source and target distributions or pay
attention to align subdomains based on multiple discriminators. However, in
real applications, the marginal (global) and conditional (local) distributions
between domains are often contributing differently to the adaptation. There is
currently no method to dynamically and quantitatively evaluate the relative
importance of these two distributions for adversarial learning. In this paper,
we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically
learn domain-invariant representations while quantitatively evaluate the
relative importance of global and local domain distributions. To the best of
our knowledge, DAAN is the first attempt to perform dynamic adversarial
distribution adaptation for deep adversarial learning. DAAN is extremely easy
to implement and train in real applications. We theoretically analyze the
effectiveness of DAAN, and it can also be explained in an attention strategy.
Extensive experiments demonstrate that DAAN achieves better classification
accuracy compared to state-of-the-art deep and adversarial methods. Results
also imply the necessity and effectiveness of the dynamic distribution
adaptation in adversarial transfer learning.
