When dealing with deep neural network (DNN) applications on edge devices, continuously updating the model is important. Although updating a model with real incoming data is ideal, using all of them is not always feasible due to limits, such as labeling and communication costs. Thus, it is necessary to filter and select the data to use for training (i.e., active learning) on the device. In this paper, we formalize a practical active learning problem for DNNs on edge devices and propose a general task-agnostic framework to tackle this problem, which reduces it to a stream submodular maximization. This framework is light enough to be run with low computational resources, yet provides solutions whose quality is theoretically guaranteed thanks to the submodular property. Through this framework, we can configure data selection criteria flexibly, including using methods proposed in previous active learning studies. We evaluate our approach on both classification and object detection tasks in a practical setting to simulate a real-life scenario. The results of our study show that the proposed framework outperforms all other methods in both tasks, while running at a practical speed on real devices.

翻译：在处理边缘设备上的深度神经网络（DNN）应用时，持续更新模型至关重要。尽管使用实时传入的数据更新模型是理想方案，但由于标注成本与通信开销等限制，并非总能利用全部数据。因此，有必要在设备上对训练数据进行筛选与选择（即主动学习）。本文形式化了边缘设备上DNN的实用主动学习问题，并提出一种通用的任务无关框架来应对该问题，将其简化为流式子模最大化。该框架计算开销极低，能在有限资源下运行，同时借助子模性质，提供理论质量保证的解决方案。通过该框架，可灵活配置数据选择准则，包括采用既往主动学习研究中的方法。我们在实际场景下对分类与目标检测任务进行了评估，以模拟真实应用环境。研究结果表明，所提框架在两项任务中均优于其他方法，且在真实设备上以实用速度运行。