  Domain adaptation is transfer learning which aims to generalize a learning
model across training and testing data with different distributions. Most
previous research tackle this problem in seeking a shared feature
representation between source and target domains while reducing the mismatch of
their data distributions. In this paper, we propose a close yet discriminative
domain adaptation method, namely CDDA, which generates a latent feature
representation with two interesting properties. First, the discrepancy between
the source and target domain, measured in terms of both marginal and
conditional probability distribution via Maximum Mean Discrepancy is minimized
so as to attract two domains close to each other. More importantly, we also
design a repulsive force term, which maximizes the distances between each label
dependent sub-domain to all others so as to drag different class dependent
sub-domains far away from each other and thereby increase the discriminative
power of the adapted domain. Moreover, given the fact that the underlying data
manifold could have complex geometric structure, we further propose the
constraints of label smoothness and geometric structure consistency for label
propagation. Extensive experiments are conducted on 36 cross-domain image
classification tasks over four public datasets. The comprehensive results show
that the proposed method consistently outperforms the state-of-the-art methods
with significant margins.
