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.
翻译:在不断变化的测试环境中使训练好的模型能够令人满意地运行是一项重要而具有挑战性的任务。在这项工作中,我们提出了一种新颖的框架 SATA,旨在满足在线自适应所需的以下特征:1)可以与不同(最好是较小的)批量大小无缝协作,以降低延迟; 2)应继续在源域上良好运作; 3)应具有最小的可调超参数和存储要求。给定在源域数据上训练的预训练网络,所提出的 SATA 框架使用基于源锚定自我蒸馏的方式修改批量归一化的仿射参数。这确保了模型融合了新遇到的领域的知识,同时不会过度遗忘以前见过的领域。我们还提出了一种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,同时保持已经学到的语义信息。在三个基准数据集上的广泛评估证实,SATA 对于实际应用是有效的。