Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Test-time Self-Learning method with automatic Adversarial augmentation dubbed TeSLA for adapting a pre-trained source model to the unlabeled streaming test data. In contrast to conventional self-learning methods based on cross-entropy, we introduce a new test-time loss function through an implicitly tight connection with the mutual information and online knowledge distillation. Furthermore, we propose a learnable efficient adversarial augmentation module that further enhances online knowledge distillation by simulating high entropy augmented images. Our method achieves state-of-the-art classification and segmentation results on several benchmarks and types of domain shifts, particularly on challenging measurement shifts of medical images. TeSLA also benefits from several desirable properties compared to competing methods in terms of calibration, uncertainty metrics, insensitivity to model architectures, and source training strategies, all supported by extensive ablations. Our code and models are available on GitHub.

翻译：大多数现有的测试时自适应方法仅针对分类任务、使用专用网络架构、破坏模型校准或依赖源域中的轻量信息。为解决这些问题，本文提出一种新颖的测试时自学习方法——TeSLA（Test-time Self-Learning with Automatic Adversarial Augmentation），该方法通过自动对抗增强将预训练源模型自适应至无标签的流式测试数据。与基于交叉熵的传统自学习方法不同，我们通过隐式紧密连接互信息与在线知识蒸馏引入了一种新的测试时损失函数。此外，我们提出了一种可学习的有效对抗增强模块，通过模拟高熵增强图像进一步强化在线知识蒸馏。我们的方法在多个基准数据集和多种域偏移类型上取得了最先进的分类与分割结果，尤其在医学图像中具有挑战性的测量偏移场景表现优异。相比于竞争方法，TeSLA在校准性、不确定性指标、对模型架构的鲁棒性以及源训练策略方面均具备多项理想特性，这些优势均通过大量消融实验得到验证。我们的代码和模型已在GitHub上公开。