Adversarial Partial Domain Adaptation by Cycle Inconsistency

Kun-Yu Lin, Jiaming Zhou, Yukun Qiu, Wei-Shi Zheng ;

"Unsupervised partial domain adaptation (PDA) is a unsupervised domain adaptation problem which assumes that the source label space subsumes the target label space. A critical challenge of PDA is the negative transfer problem, which is triggered by learning to match the whole source and target domains. To mitigate negative transfer, we note a fact that, it is impossible for a source sample of outlier classes to find a target sample of the same category due to the absence of outlier classes in the target domain, while it is possible for a source sample of shared classes. Inspired by this fact, we exploit the cycle inconsistency, i.e., category discrepancy between the original features and features after cycle transformations, to distinguish outlier classes apart from shared classes in the source domain. Accordingly, we propose to filter out source samples of outlier classes by weight suppression and align the distributions of shared classes between the source and target domains by adversarial learning. To learn accurate weight assignment for filtering out outlier classes, we design cycle transformations based on domain prototypes and soft nearest neighbor, where center losses are introduced in individual domains to reduce the intra-class variation. Experiment results on three benchmark datasets demonstrate the effectiveness of our proposed method."