Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
This addresses performance degradation in machine learning models when deployed on new, unlabeled data distributions, representing a strong incremental improvement.
The paper tackles test-time adaptation under distribution shift by proposing shift-agnostic weight regularization and nearest source prototypes, achieving state-of-the-art performance on benchmarks and even outperforming supervised methods.
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to alleviate the performance degradation due to the distribution shift between the source and target domains. Adapting the entire model parameters using the unlabeled online data may be detrimental due to the erroneous signals from an unsupervised objective. To mitigate this problem, we propose a shift-agnostic weight regularization that encourages largely updating the model parameters sensitive to distribution shift while slightly updating those insensitive to the shift, during test-time adaptation. This regularization enables the model to quickly adapt to the target domain without performance degradation by utilizing the benefit of a high learning rate. In addition, we present an auxiliary task based on nearest source prototypes to align the source and target features, which helps reduce the distribution shift and leads to further performance improvement. We show that our method exhibits state-of-the-art performance on various standard benchmarks and even outperforms its supervised counterpart.