Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation

Abstract

Unsupervised domain adaptation (UDA) aims to learn a prediction model for the target domain given labeled source data and unlabeled target data. Impressive progress has been made by adversarial learning-based methods that align distributions across domains through deceiving a domain discriminator network. However, these methods only try to align two domains and neglect the boundaries between classes, which may lead to false alignment and poor generalization performance. In contrast, consistency-enforcing methods exploit the target data posterior distribution to make the target features far away from decision boundaries. Despite their efficacy, these approaches require additional intensity augmentation to align distributions when encountering datasets with large domain discrepancy. To solve the above problems, we propose a novel UDA method that unifies the adversarial learning-based method and consistency-enforcing method together to take both domain alignment and boundaries between classes into consideration. In addition to the supervised classification on the source domain and the adversarial domain adaptation, we introduce interpolation consistency into the UDA task. To be specific, we first construct robust and informative pseudo labels for target samples, and then we encourage the prediction at an interpolation of unlabeled target samples to be consistent with the interpolation of the pseudo labels of these samples. The extensive empirical results demonstrate that our method achieves state-of-the-art results on both digit classification and object recognition tasks.