通过融合熵与梯度范数提升测试时自适应的稳定性与鲁棒性。
ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance
- 结合熵与梯度范数筛选更可靠的样本进行自适应。
- 在CIFAR-10-C和CIFAR-100-C上显著优于现有方法。
- 特别适用于存在偏差或分布外的数据场景。
测试时自适应（TTA）使深度学习模型能够在源数据不可用的情况下，应对偏离训练分布的未见测试数据。传统方法常依赖熵作为置信度指标，但在偏差场景下效果有限。我们基于伪标签概率差（PLPD）提出ETAGE，将熵最小化与梯度范数、PLPD相结合，优化样本选择与自适应过程。该方法优先选择在适应前具有高熵与高梯度范数的样本，避免对噪声的过拟合，从而提升稳定性。在CIFAR-10-C和CIFAR-100-C上的大量实验表明，该方法在挑战性与偏差场景中均显著优于现有TTA技术，实现更稳健一致的模型表现。代码已开源：https://github.com/afsharshamsi/ETAGE。
原文摘要 · Abstract (English)
Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution, even when source data is inaccessible. While traditional TTA methods often rely on entropy as a confidence metric, its effectiveness can be limited, particularly in biased scenarios. Extending existing approaches like the Pseudo Label Probability Difference (PLPD), we introduce ETAGE, a refined TTA method that integrates entropy minimization with gradient norms and PLPD, to enhance sample selection and adaptation. Our method prioritizes samples that are less likely to cause instability by combining high entropy with high gradient norms out of adaptation, thus avoiding the overfitting to noise often observed in previous methods. Extensive experiments on CIFAR-10-C and CIFAR-100-C datasets demonstrate that our approach outperforms existing TTA techniques, particularly in challenging and biased scenarios, leading to more robust and consistent model performance across diverse test scenarios. The codebase for ETAGE is available on https://github.com/afsharshamsi/ETAGE.
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