  Current deep domain adaptation methods used in computer vision have mainly
focused on learning discriminative and domain-invariant features across
different domains. In this paper, we present a novel "deep adversarial
transition learning" (DATL) framework that bridges the domain gap by projecting
the source and target domains into intermediate, transitional spaces through
the employment of adjustable, cross-grafted generative network stacks and
effective adversarial learning between transitions. Specifically, we construct
variational auto-encoders (VAE) for the two domains, and form bidirectional
transitions by cross-grafting the VAEs' decoder stacks. Furthermore, generative
adversarial networks (GAN) are employed for domain adaptation, mapping the
target domain data to the known label space of the source domain. The overall
adaptation process hence consists of three phases: feature representation
learning by VAEs, transitions generation, and transitions alignment by GANs.
Experimental results demonstrate that our method outperforms the state-of-the
art on a number of unsupervised domain adaptation benchmarks.
