[2603.26096] AcTTA: Rethinking Test-Time Adaptation via Dynamic Activation

Computer Science > Machine Learning arXiv:2603.26096 (cs) [Submitted on 27 Mar 2026] Title:AcTTA: Rethinking Test-Time Adaptation via Dynamic Activation Authors:Hyeongyu Kim, Geonhui Han, Dosik Hwang View a PDF of the paper titled AcTTA: Rethinking Test-Time Adaptation via Dynamic Activation, by Hyeongyu Kim and 2 other authors View PDF HTML (experimental) Abstract:Test-time adaptation (TTA) aims to mitigate performance degradation under distribution shifts by updating model parameters during inference. Existing approaches have primarily framed adaptation around affine modulation, focusing on recalibrating normalization layers. This perspective, while effective, overlooks another influential component in representation dynamics: the activation function. We revisit this overlooked space and propose AcTTA, an activation-aware framework that reinterprets conventional activation functions from a learnable perspective and updates them adaptively at test time. AcTTA reformulates conventional activation functions (e.g., ReLU, GELU) into parameterized forms that shift their response threshold and modulate gradient sensitivity, enabling the network to adjust activation behavior under domain shifts. This functional reparameterization enables continuous adjustment of activation behavior without modifying network weights or requiring source data. Despite its simplicity, AcTTA achieves robust and stable adaptation across diverse corruptions. Across CIFAR10-C, CIFAR100-C, and ImageNet-C...