Advancing Model Generalization in Continuous Cyclic Test-Time Adaptation with Matrix Perturbation Noise

Abstract

Test-time adaptation (TTA) aims to optimize source-pretrained model parameters to target domains using only unlabeled test data. However, traditional TTA methods often risk overfitting to the specific, localized test domains, leading to compromised generalization. Moreover, these methods generally presume static target domains, neglecting the dynamic and cyclic nature of real-world settings. To alleviate this limitation, this paper explores the continuous cyclic test-time adaptation (CycleTTA) setting. Our unique approach within this setting employs matrix-wise perturbation noise in batch-normalization statistics to enhance the adaptability of source-pretrained models to dynamically changing target domains, without the need for additional parameters. We demonstrated the effectiveness of our method through extensive experiments, where our approach reduced the average error by 39.8% on the CIFAR10-C dataset using the WideResNet-28-10 model, by 38.8% using the WideResNet-40-2 model, and by 33.8% using the PreActResNet-18 model. Additionally, on the CIFAR100-C dataset with the WideResNet-40-2 model, our method reduced the average error by 5.3%, showcasing significant improvements in model generalization in continuous cyclic testing scenarios.