  A good feature representation is the key to image classification. In
practice, image classifiers may be applied in scenarios different from what
they have been trained on. This so-called domain shift leads to a significant
performance drop in image classification. Unsupervised domain adaptation (UDA)
reduces the domain shift by transferring the knowledge learned from a labeled
source domain to an unlabeled target domain. We perform feature disentanglement
for UDA by distilling category-relevant features and excluding
category-irrelevant features from the global feature maps. This disentanglement
prevents the network from overfitting to category-irrelevant information and
makes it focus on information useful for classification. This reduces the
difficulty of domain alignment and improves the classification accuracy on the
target domain. We propose a coarse-to-fine domain adaptation method called
Domain Adaptation via Feature Disentanglement~(DAFD), which has two components:
(1)the Category-Relevant Feature Selection (CRFS) module, which disentangles
the category-relevant features from the category-irrelevant features, and
(2)the Dynamic Local Maximum Mean Discrepancy (DLMMD) module, which achieves
fine-grained alignment by reducing the discrepancy within the category-relevant
features from different domains. Combined with the CRFS, the DLMMD module can
align the category-relevant features properly. We conduct comprehensive
experiment on four standard datasets. Our results clearly demonstrate the
robustness and effectiveness of our approach in domain adaptive image
classification tasks and its competitiveness to the state of the art.
