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

Researchers at an undisclosed institution introduced Reliable Neural Collapse approximation (ReNC), a method for Open-World Test-Time Adaptation (OWTTA) that uses neural collapse as a structural prior to filter out-of-distribution samples and refine prototypes for reliable target-domain adaptation. In experiments on several open-world benchmarks, ReNC outperformed existing TTA methods and better preserved neural collapse properties, with code available at https://github.com/JiaqiLin-AI/ReNC.

arXiv:2608.19890v1 Announce Type: new 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.