  In many practical transfer learning scenarios, the feature distribution is
different across the source and target domains (i.e. non-i.i.d.). Maximum mean
discrepancy (MMD), as a domain discrepancy metric, has achieved promising
performance in unsupervised domain adaptation (DA). We argue that MMD-based DA
methods ignore the data locality structure, which, to some extent, would cause
the negative transfer effect. The locality plays an important role in
minimizing the nonlinear local domain discrepancy underlying the marginal
distributions. For better exploiting the domain locality, a novel local
generative discrepancy metric (LGDM) based intermediate domain generation
learning called Manifold Criterion guided Transfer Learning (MCTL) is proposed
in this paper. The merits of the proposed MCTL are four-fold: 1) the concept of
manifold criterion (MC) is first proposed as a measure validating the
distribution matching across domains, and domain adaptation is achieved if the
MC is satisfied; 2) the proposed MC can well guide the generation of the
intermediate domain sharing similar distribution with the target domain, by
minimizing the local domain discrepancy; 3) a global generative discrepancy
metric (GGDM) is presented, such that both the global and local discrepancy can
be effectively and positively reduced; 4) a simplified version of MCTL called
MCTL-S is presented under a perfect domain generation assumption for more
generic learning scenario. Experiments on a number of benchmark visual transfer
tasks demonstrate the superiority of the proposed manifold criterion guided
generative transfer method, by comparing with other state-of-the-art methods.
The source code is available in https://github.com/wangshanshanCQU/MCTL.
