Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation

• URL: http://arxiv.org/abs/2608.19890v1
• Date: Thu, 20 Aug 2026 10:57:44 GMT
• Title: Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation
• Abstract summary: Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains.<n>Traditional TTA methods become ineffective when the label distribution shift occurs.<n>We introduce a new method named Reliable Neural Collapse approximation (ReNC) for Open-World Test-Time Adaptation.
• Score: 95.55552104238787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains. However, traditional TTA methods become ineffective when the label distribution shift occurs, a challenge commonly referred to as an open-world scenario. In this paper, we introduce a new method named Reliable Neural Collapse approximation (ReNC) for Open-World Test-Time Adaptation (OWTTA). Specifically, we leverage neural collapse as a structural prior for reliable target-domain adaptation. Guided by this prior, we justify that the pre-trained classifier weights can serve as the prototypes of the source domain. By measuring the similarity between samples and prototypes, we filter out the Out-Of-Distribution~(OOD) samples for reliable updates. Furthermore, we propose a neural collapse approximation mechanism to refine these prototypes, ensuring they can gradually adapt to the target domain while maintaining the neural collapse structure. Extensive experiments on several open-world benchmarks demonstrate the superiority of the proposed method. Our empirical analysis suggests that ReNC better preserves NC-related properties in the target domain, providing useful evidence for explaining reliable OWTTA and offering new insights for model design. Code is available at https://github.com/JiaqiLin-AI/ReNC.

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