Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment

• URL: http://arxiv.org/abs/2303.04393v2
• Date: Thu, 9 Mar 2023 01:29:06 GMT
• Title: Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment
• Authors: Jinghan Ru and Jun Tian and Zhekai Du and Chengwei Xiao and Jingjing Li and Heng Tao Shen
• Abstract summary: Open Set Domain Adaptation (OSDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain. The performance of OSDA methods degrades drastically under intra-domain class imbalance and inter-domain label shift. We propose Open-set Moving-threshold Estimation and Gradual Alignment (OMEGA) to alleviate the negative effects raised by label shift.
• Score: 58.56087979262192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimedia applications are often associated with cross-domain knowledge transfer, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain shifts. Open Set Domain Adaptation (OSDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain under the assumption that the target domain contains unknown classes. Existing OSDA methods consistently lay stress on the covariate shift, ignoring the potential label shift problem. The performance of OSDA methods degrades drastically under intra-domain class imbalance and inter-domain label shift. However, little attention has been paid to this issue in the community. In this paper, the Imbalanced Open Set Domain Adaptation (IOSDA) is explored where the covariate shift, label shift and category mismatch exist simultaneously. To alleviate the negative effects raised by label shift in OSDA, we propose Open-set Moving-threshold Estimation and Gradual Alignment (OMEGA) - a novel architecture that improves existing OSDA methods on class-imbalanced data. Specifically, a novel unknown-aware target clustering scheme is proposed to form tight clusters in the target domain to reduce the negative effects of label shift and intra-domain class imbalance. Furthermore, moving-threshold estimation is designed to generate specific thresholds for each target sample rather than using one for all. Extensive experiments on IOSDA, OSDA and OPDA benchmarks demonstrate that our method could significantly outperform existing state-of-the-arts. Code and data are available at https://github.com/mendicant04/OMEGA.

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