Importance-weighted conditional adversarial network for unsupervised domain adaptation

Abstract

In the construction of expert and intelligent systems, annotating and curating large datasets is very expensive; hence, there is a need to transfer the knowledge from existing annotated datasets to unlabeled data. However, data that are relevant for a specific application usually differ from publicly available datasets because they are sampled from a different domain. Domain adaptation (DA) has emerged as an efficient technique to compensate for such a domain shift. Recent studies have suggested that deep adversarial networks can achieve promising results for DA problems. However, existing adversarial DA methods assign equal importance to different examples and ignore the effect of difference in source domain samples or noise on adversarial performance. Moreover, most DA methods only focus on reducing the distribution difference, but not to learn a good target domain model. To address these issues, we propose an importance-weighted conditional adversarial (IWCA) network for unsupervised DA. In this study, an importance criterion based on domain similarity and prediction certainty is proposed to assign weights to different samples, which can reduce the harmful effects of difficult-to-transfer samples when reducing their cross-domain class conditional distribution differences. Furthermore, a sample selection criterion derived from the perspective of transfer cross validation is used to progressively select appropriate pseudo-labeled target samples to fine-tune the target model. These two criteria work in an EM-like manner that alternating align class conditional distribution for weighted samples and progressively select certain pseudo-labeled target samples to fine-tune the joint model. In this manner, the network will gradually generate features that approximate the actual conditional distribution of the target domain. The results of extensive experiments conducted on four datasets show that IWCA outperforms several state-of-the-art deep DA methods.