Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applications with online machine learning models can handle this issue at test time, albeit with high cost and unreliable performance. To overcome this limitation, we propose to endow test-time adaptation with a systematic active fine-tuning (SAF) layer that is characterized by three key aspects: a continuity aspect that adapts to ever-present data distribution shifts; intelligence aspect that recognizes the importance of fine-tuning as a distribution-shift-aware process that occurs at the appropriate time to address the recently detected data distribution shifts; and cost-effectiveness aspect that involves budgeted human-machine collaboration to make relabeling cost-effective and practical for diverse smart city applications. Our empirical results show that our proposed approach outperforms the traditional test-time adaptation by a factor of two.

翻译：数据分布漂移是机器学习驱动的智慧城市应用中常见的问题，即测试数据与训练数据存在差异。通过在线机器学习模型增强智慧城市应用虽可在测试时解决此问题，但存在成本高昂且性能不稳定的缺陷。为克服这一局限，我们提出为测试时自适应赋予系统性主动微调（SAF）层，其包含三个关键维度：连续性维度——适应持续存在的数据分布漂移；智能性维度——将微调识别为依赖于分布漂移的过程，在适当时机针对新检测到的数据分布漂移进行干预；成本效益维度——通过预算约束下的人机协作，使重标记过程兼具成本效益与实用性，从而适用于多样化的智慧城市场景。实验结果表明，我们提出的方法相比传统测试时自适应性能提升两倍。