论文标题
通过移位不足的重量正则化和最近的原型来改善测试时间适应
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
论文作者
论文摘要
本文提出了一种新型的测试时间适应策略，该策略仅使用来自目标域的未标记的在线数据来调整在源域上预先训练的模型，以减轻由于源域和目标域之间的分布变化而导致的性能下降。使用未标记的在线数据调整整个模型参数可能是由于无监督目标的错误信号而有害的。为了减轻此问题，我们提出了一个偏斜的权重正则化，该调整重量正规化鼓励在很大程度上更新模型参数对分布偏移敏感的参数，同时在测试时间适应期间稍微更新对移位的不敏感的参数。这种正则化使该模型能够通过利用高学习率的好处来快速适应目标域而无需性能降低。此外，我们提出了一个基于最近的源原型来对齐源和目标特征的辅助任务，这有助于减少分布转移并导致进一步的性能提高。我们表明，我们的方法在各种标准基准上展示了最先进的性能，甚至表现出优于其受监督的对手。
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.
