一次蒸馏，终身适应：探索用于持续测试时适应的数据集蒸馏

摘要

持续测试时自适应（CTTA）旨在通过在线无标签数据适应不断变化的目标域，从而维持模型性能。然而，实际部署中常因隐私或许可限制无法保留源数据集，而纯无源CTTA方法在长期分布偏移下易变得不稳定，面临自训练错误累积与灾难性遗忘问题。我们提出DO-ALL（一次蒸馏，终身自适应）——一种即插即用框架，通过数据集蒸馏（DD）以紧凑且隐私友好的形式重新利用源信息。部署前，DO-ALL执行DD以生成少量合成蒸馏锚点，概括源分布特征。自适应阶段，每个目标样本与语义最匹配的锚点对齐，该锚点通过源回放、表征对齐及流形平滑正则化为各类CTTA提供稳定参考。DO-ALL可无缝集成至现有CTTA算法中，在CIFAR100-C、ImageNet-C及CCC基准上持续提升长期鲁棒性。这证明了利用DD实现无需保留原始源数据的稳定持续自适应的潜力。代码已开源：https://github.com/blue-531/DOALL。

English

Continual Test-Time Adaptation (CTTA) aims to maintain model performance under evolving target domains by adapting online without labeled data. However, practical deployments often cannot retain the source dataset due to privacy or licensing constraints, and purely source-free CTTA methods tend to become unstable under long-term distribution shift, suffering from compounding self-training errors and catastrophic forgetting. We introduce DO-ALL (Distill Once, Adapt Life-Long), a plug-and-play framework that revisits source information in a compact and privacy-conscious form via Dataset Distillation (DD). Before deployment, DO-ALL performs DD to produce a small set of synthetic distilled anchors that summarize the source distribution. During adaptation, each target sample is matched with its most semantically aligned anchor, which provides a stable reference for various CTTA via source replay, representation alignment, and manifold-smoothing regularization. DO-ALL can be seamlessly integrated into existing CTTA algorithms, consistently improving long-term robustness across CIFAR100-C, ImageNet-C, and the CCC benchmark. This demonstrates the potential of leveraging DD to enable stable and continuous adaptation without retaining raw source data. The code is available at https://github.com/blue-531/DOALL.