Cooperative and Adversarial Learning: Co-Enhancing Discriminability and Transferability in Domain Adaptation

Abstract

Discriminability and transferability are two goals of feature learning for domain adaptation (DA), as we aim to find the transferable features from the source domain that are helpful for discriminating the class label in the target domain. Modern DA approaches optimize discriminability and transferability by adopting two separate modules for the two goals upon a feature extractor, but lack fully exploiting their relationship. This paper argues that by letting the discriminative module and transfer module help each other, better DA can be achieved. We propose Cooperative and Adversarial LEarning (CALE) to combine the optimization of discriminability and transferability into a whole, provide one solution for making the discriminative module and transfer module guide each other. Specifically, CALE generates cooperative (easy) examples and adversarial (hard) examples with both discriminative module and transfer module. While the easy examples that contain the module knowledge can be used to enhance each other, the hard ones are used to enhance the robustness of the corresponding goal. Experimental results show the effectiveness of CALE for unifying the learning of discriminability and transferability, as well as its superior performance.