STAG: Structural Test-time Alignment of Gradients for Online Adaptation

Abstract

Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose StructuralTest-time Alignment of Gradients (STAG), a lightweight plug-in enhancer that exploits an always-available structural signal: the classifier's intrinsic geometry. STAG derives class-wise structural anchors from classifier weights via self-structural entropy, and during adaptation analytically computes the predicted-class entropy gradient from forward-pass quantities, aligning it to the corresponding anchor with a cosine-similarity loss. This closed-form design incurs near-zero memory and latency overhead and requires no additional backpropagation beyond the underlying baseline. Across corrupted image classification and continual semantic segmentation, STAG provides broadly applicable performance gains for strong TTA baselines on both CNN and Transformer architectures regardless of the underlying normalization scheme, with particularly large gains under challenging online regimes such as imbalanced label shifts, single-sample adaptation, mixed corruption streams and long-horizon continual TTA.