This paper presents a simple yet effective approach that improves continual test-time adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge devices with limited memory, so reducing memory is crucial but has been overlooked in previous TTA studies. In addition, long-term adaptation often leads to catastrophic forgetting and error accumulation, which hinders applying TTA in real-world deployments. Our approach consists of two components to address these issues. First, we present lightweight meta networks that can adapt the frozen original networks to the target domain. This novel architecture minimizes memory consumption by decreasing the size of intermediate activations required for backpropagation. Second, our novel self-distilled regularization controls the output of the meta networks not to deviate significantly from the output of the frozen original networks, thereby preserving well-trained knowledge from the source domain. Without additional memory, this regularization prevents error accumulation and catastrophic forgetting, resulting in stable performance even in long-term test-time adaptation. We demonstrate that our simple yet effective strategy outperforms other state-of-the-art methods on various benchmarks for image classification and semantic segmentation tasks. Notably, our proposed method with ResNet-50 and WideResNet-40 takes 86% and 80% less memory than the recent state-of-the-art method, CoTTA.

翻译：本文提出了一种简单而有效的方法，以内存高效的方式改进了持续测试时适应（TTA）。TTA主要可能在内存有限的边缘设备上进行，因此减少内存消耗至关重要，但以往TTA研究对此有所忽视。此外，长期适应往往导致灾难性遗忘和误差累积，阻碍了TTA在实际部署中的应用。我们的方法包含两个组件来解决这些问题。首先，我们提出了轻量级元网络，能够将冻结的原始网络适应到目标领域。这种新颖架构通过减少反向传播所需的中间激活大小，最小化了内存消耗。其次，我们创新的自蒸馏正则化控制元网络的输出，使其与冻结原始网络的输出保持较小偏差，从而保留来自源领域的良好训练知识。无需额外内存，这种正则化能防止误差累积和灾难性遗忘，即使在长期测试时适应中也能保持稳定的性能。我们证明，这种简单而有效的策略在图像分类和语义分割任务的各种基准测试中优于其他最先进方法。值得注意的是，我们提出的方法在使用ResNet-50和WideResNet-40时，相比于近期最先进的CoTTA方法，分别减少了86%和80%的内存消耗。