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

Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation