Dual teacher–student based separation mechanism for open set domain adaptation

Abstract

Open set domain adaptation (OSDA) is a more realistic and challenging domain adaptation protocol that assumes that the label spaces of source and target domains are different. Existing OSDA methods lack the ability to identify unknown samples and suffer more from negative transfer. To mitigate the negative transfer, we propose a dual teacher–student based sample separation mechanism to accurately identify and separate known and unknown samples in the target domain. Specifically, in the teacher–student structure (TS), students are insensitive to target unknown samples because they learn only source domain knowledge. Therefore, we exploit this property of students to construct a dual teacher–student structure (DTS), which quantifies the similarity between the target samples and the source domain and improves the accuracy and robustness of the similarity assessment. In addition, we further build a selective adversarial learning model by weighting the target samples with the weights derived from the similarity assessment. This promotes cross-domain alignment of the distribution of target samples with higher weights and the distribution of source samples. Extensive experiments conducted on the benchmark dataset demonstrate the significant advantages of the proposed approach over the extant methods.