MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation

Test-Time Adaptation (TTA) methods commonly updatethe affine parameters of normalization layers to adapt deployed modelsunder distribution shifts. However, per-channel affine parameters per-form axis-aligned scaling and shifting, making them geometrically inca-pable of correcting cross-channel structural changes induced by distribu-tion shift. To address this limitation, we propose MixTTA, a lightweightplug-in module that equips normalization layers with a low-rank cross-channel transformation, enabling inter-channel mixing at each layer. Toensure that the low-rank branch captures only cross-channel interac-tions, we also propose Decoupling Projection that enforces strict separa-tion from the diagonal affine path, along with Spectral Projection thatprevents rank-1 collapse under non-stationary test streams. MixTTAcan be seamlessly integrated into any existing normalization-based TTAmethod. Experiments in both standard and wild TTA settings show con-sistent improvements over strong baselines while mitigating adaptationfailure under challenging conditions. The source code is publicly availableat https://github.com/delta6189/MixTTA.