We study the problem of continual test-time adaption where the goal is to adapt a source pre-trained model to a sequence of unlabelled target domains at test time. Existing methods on test-time training suffer from several limitations: (1) Mismatch between the feature extractor and classifier; (2) Interference between the main and self-supervised tasks; (3) Lack of the ability to quickly adapt to the current distribution. In light of these challenges, we propose a cascading paradigm that simultaneously updates the feature extractor and classifier at test time, mitigating the mismatch between them and enabling long-term model adaptation. The pre-training of our model is structured within a meta-learning framework, thereby minimizing the interference between the main and self-supervised tasks and encouraging fast adaptation in the presence of limited unlabelled data. Additionally, we introduce innovative evaluation metrics, average accuracy and forward transfer, to effectively measure the model's adaptation capabilities in dynamic, real-world scenarios. Extensive experiments and ablation studies demonstrate the superiority of our approach in a range of tasks including image classification, text classification, and speech recognition.

研究了在测试时间内将源预训练模型适应一系列未标记目标领域的问题，并提出了一种级联范式，同时在测试时间内更新特征提取器和分类器，减轻它们之间的不匹配，并在有限未标记数据的情况下鼓励快速适应。通过创新的评估指标，如平均准确率和前向传递，有效衡量模型在动态真实场景下的适应能力，并通过广泛的实验证明了方法在图像分类、文本分类和语音识别等一系列任务中的优越性。

自适应级联网络用于连续测试时间适应