MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation
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Computer Science > Machine Learning
Title:MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation
Abstract:Test-Time Adaptation (TTA) methods commonly update the affine parameters of normalization layers to adapt deployed models under distribution shifts. However, per-channel affine parameters perform axis-aligned scaling and shifting, making them geometrically incapable of correcting cross-channel structural changes induced by distribution shift. To address this limitation, we propose MixTTA, a lightweight plug-in module that equips normalization layers with a low-rank cross-channel transformation, enabling inter-channel mixing at each layer. To ensure that the low-rank branch captures only cross-channel interactions, we also propose Decoupling Projection that enforces strict separation from the diagonal affine path, along with Spectral Projection that prevents rank-1 collapse under non-stationary test streams. MixTTA can be seamlessly integrated into any existing normalization-based TTA method. Experiments in both standard and wild TTA settings show consistent improvements over strong baselines while mitigating adaptation failure under challenging conditions. The source code is publicly available at this https URL.
| Comments: | To be published in the 19th European Conference on Computer Vision -- ECCV 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.28142 [cs.LG] |
| (or arXiv:2606.28142v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.28142
arXiv-issued DOI via DataCite (pending registration)
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