De-Confusing Pseudo-Labels in Source-Free Domain Adaptation

• URL: http://arxiv.org/abs/2401.01650v3
• Date: Thu, 31 Oct 2024 16:53:49 GMT
• Title: De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
• Authors: Idit Diamant, Amir Rosenfeld, Idan Achituve, Jacob Goldberger, Arnon Netzer,
• Abstract summary: Source-free domain adaptation aims to adapt a source-trained model to an unlabeled target domain without access to the source data. We introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings.
• Score: 14.954662088592762
• License:
• Abstract: Source-free domain adaptation aims to adapt a source-trained model to an unlabeled target domain without access to the source data. It has attracted growing attention in recent years, where existing approaches focus on self-training that usually includes pseudo-labeling techniques. In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels. More specifically, we learn a noise transition matrix of the pseudo-labels to capture the label corruption of each class and learn the underlying true label distribution. Estimating the noise transition matrix enables a better true class-posterior estimation, resulting in better prediction accuracy. We demonstrate the effectiveness of our approach when combined with several source-free domain adaptation methods: SHOT, SHOT++, and AaD. We obtain state-of-the-art results on three domain adaptation datasets: VisDA, DomainNet, and OfficeHome.

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