  Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled
source domain to the unlabeled but related target domain without requiring
large amounts of target supervision. Recent advances in DA mainly proceed by
aligning the source and target distributions. Despite the significant success,
the adaptation performance still degrades accordingly when the source and
target domains encounter a large distribution discrepancy. We consider this
limitation may attribute to the insufficient exploration of domain-specialized
features because most studies merely concentrate on domain-general feature
learning in task-specific layers and integrate totally-shared convolutional
networks (convnets) to generate common features for both domains. In this
paper, we relax the completely-shared convnets assumption adopted by previous
DA methods and propose Domain Conditioned Adaptation Network (DCAN), which
introduces domain conditioned channel attention module with a multi-path
structure to separately excite channel activation for each domain. Such a
partially-shared convnets module allows domain-specialized features in
low-level to be explored appropriately. Further, given the knowledge
transferability varying along with convolutional layers, we develop Generalized
Domain Conditioned Adaptation Network (GDCAN) to automatically determine
whether domain channel activations should be separately modeled in each
attention module. Afterward, the critical domain-specialized knowledge could be
adaptively extracted according to the domain statistic gaps. As far as we know,
this is the first work to explore the domain-wise convolutional channel
activations separately for deep DA networks. Additionally, to effectively match
high-level feature distributions across domains, we consider deploying feature
adaptation blocks after task-specific layers, which can explicitly mitigate the
domain discrepancy.
