Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
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 …