Adapting a trained model to perform satisfactorily on continually changing testing domains/environments is an important and challenging task. In this work, we propose a novel framework, SATA, which aims to satisfy the following characteristics required for online adaptation: 1) can work seamlessly with different (preferably small) batch sizes to reduce latency; 2) should continue to work well for the source domain; 3) should have minimal tunable hyper-parameters and storage requirements. Given a pre-trained network trained on source domain data, the proposed SATA framework modifies the batch-norm affine parameters using source anchoring based self-distillation. This ensures that the model incorporates the knowledge of the newly encountered domains, without catastrophically forgetting about the previously seen ones. We also propose a source-prototype driven contrastive alignment to ensure natural grouping of the target samples, while maintaining the already learnt semantic information. Extensive evaluation on three benchmark datasets under challenging settings justify the effectiveness of SATA for real-world applications.

本文提出一种基于在线自适应的新框架 SATA，它可以无缝地适应不同的批处理大小，同时通过使用源锚定的自蒸馏来修改批归一化的仿射参数，来确保模型具有不遗忘以前经历的领域知识的能力。同时，提出基于源原型驱动的对比对齐方法，以确保目标样本的自然分组，同时维护已经学习的语义信息。在三个基准数据集上的 extensive evaluation 证明了 SATA 在实际应用中的有效性。

SATA: 源锚定和目标对齐网络用于连续测试时间自适应