  Recent works on domain adaptation exploit adversarial training to obtain
domain-invariant feature representations from the joint learning of feature
extractor and domain discriminator networks. However, domain adversarial
methods render suboptimal performances since they attempt to match the
distributions among the domains without considering the task at hand. We
propose Drop to Adapt (DTA), which leverages adversarial dropout to learn
strongly discriminative features by enforcing the cluster assumption.
Accordingly, we design objective functions to support robust domain adaptation.
We demonstrate efficacy of the proposed method on various experiments and
achieve consistent improvements in both image classification and semantic
segmentation tasks. Our source code is available at
https://github.com/postBG/DTA.pytorch.
