De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
- URL: http://arxiv.org/abs/2401.01650v2
- Date: Wed, 13 Mar 2024 13:13:57 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 (SFDA) 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.
We demonstrate the effectiveness of our approach when combined with several SFDA methods: SHOT, SHOT++, and AaD.
- Score: 16.04404871688151
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Source-free domain adaptation (SFDA) aims to adapt a source-trained model to
an unlabeled target domain without access to the source data. SFDA 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 SFDA 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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