Unsupervised domain adaptation aims to generalize the supervised model
trained on a source domain to an unlabeled target domain. Marginal distribution
alignment of feature spaces is widely used to reduce the domain discrepancy
between the source and target domains. However, it assumes that the source and
target domains share the same label distribution, which limits their
application scope. In this paper, we consider a more general application
scenario where the label distributions of the source and target domains are not
the same. In this scenario, marginal distribution alignment-based methods will
be vulnerable to negative transfer. To address this issue, we propose a novel
unsupervised domain adaptation method, Deep Conditional Adaptation Network
(DCAN), based on conditional distribution alignment of feature spaces. To be
specific, we reduce the domain discrepancy by minimizing the Conditional
Maximum Mean Discrepancy between the conditional distributions of deep features
on the source and target domains, and extract the discriminant information from
target domain by maximizing the mutual information between samples and the
prediction labels. In addition, DCAN can be used to address a special scenario,
Partial unsupervised domain adaptation, where the target domain category is a
subset of the source domain category. Experiments on both unsupervised domain
adaptation and Partial unsupervised domain adaptation show that DCAN achieves
superior classification performance over state-of-the-art methods. In
particular, DCAN achieves great improvement in the tasks with large difference
in label distributions (6.1\% on SVHN to MNIST, 5.4\% in UDA tasks on
Office-Home and 4.5\% in Partial UDA tasks on Office-Home)