StableTTA: Training-Free Test-Time Adaptation that Improves Model Accuracy on ImageNet1K to 96%

arXiv:2604.04552v2 Announce Type: replace Abstract: Ensemble methods are widely used to improve predictive performance, but their effectiveness often comes at the cost of increased memory usage and computational complexity. In this paper, we identify a conflict in aggregation strategies that negatively impacts prediction stability. We propose test-time adaptation (StableTTA), a training-free method employs novel image and logit processing. Empirical results on ImageNet-1K show gains of 10.93\%-32.82\% in top-1 accuracy, with 33 models achieving over 95\% accuracy and several surpassing 96\%. Notably, StableTTA allows lightweight architectures to outperform ViT by 11.75\% in top-1 accuracy while reducing parameter count and computational cost by 97.1\% and 89.1\%, respectively, enabling high-accuracy inference on resource-constrained devices. Code is available at: https://github.com/LizhengMathAi/StableTTA, including a 3-minute reproduction demo.