Delegated Adversarial Training for Unsupervised Domain Adaptation

Abstract

In this paper, we tackle unsupervised domain adaptation, where a target domain is unlabeled and lies on a considerably different distribution from a source domain. To alleviate such data discrepancies, we coin a novel deep neural network architecture that consists of a classifier and a domain discriminator on top of a shared feature extractor. Toward efficient regularization, we delegate a generation of the adversarial attacks to the domain discriminator. We then leverage the domain adversarial images to let the classification network learn important semantic features across the domains. Specifically, we employ consistency loss function that enables the joint use of clean and adversarial data. We present extensive experimental results on various domain adaptation benchmarks to show the efficacy of the proposed method.