论文标题
通过增强测试时间适应在线机器学习中的数据分配变化驱动的智能城市应用程序
Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation
论文作者
论文摘要
数据分配转移是机器学习驱动的智能城市应用程序的常见问题，在该应用程序中，测试数据与培训数据不同。通过在线机器学习模型增强智能城市应用程序可以在测试时处理此问题，尽管成本高和不可靠的性能。为了克服这一限制，我们建议将测试时间适应赋予以系统的有效微调（SAF）层的形式，该层以三个关键方面为特征：适应不断出现的数据分布变化的连续性方面；智能方面认识到微调作为在适当时间解决最近检测到的数据分布变化的适当时间发生的分配瞬变过程的重要性；以及涉及预算的人机合作的成本效益方面，以使重新标记成具有成本效益和实用性，可用于多样化的智能城市应用。我们的经验结果表明，我们提出的方法的表现优于传统的测试时间适应性。
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.
