Continual learning approaches help deep neural network models adapt and learn incrementally by trying to solve catastrophic forgetting. However, whether these existing approaches, applied traditionally to image-based tasks, work with the same efficacy to the sequential time series data generated by mobile or embedded sensing systems remains an unanswered question. To address this void, we conduct the first comprehensive empirical study that quantifies the performance of three predominant continual learning schemes (i.e., regularization, replay, and replay with examples) on six datasets from three mobile and embedded sensing applications in a range of scenarios having different learning complexities. More specifically, we implement an end-to-end continual learning framework on edge devices. Then we investigate the generalizability, trade-offs between performance, storage, computational costs, and memory footprint of different continual learning methods. Our findings suggest that replay with exemplars-based schemes such as iCaRL has the best performance trade-offs, even in complex scenarios, at the expense of some storage space (few MBs) for training examples (1% to 5%). We also demonstrate for the first time that it is feasible and practical to run continual learning on-device with a limited memory budget. In particular, the latency on two types of mobile and embedded devices suggests that both incremental learning time (few seconds - 4 minutes) and training time (1 - 75 minutes) across datasets are acceptable, as training could happen on the device when the embedded device is charging thereby ensuring complete data privacy. Finally, we present some guidelines for practitioners who want to apply a continual learning paradigm for mobile sensing tasks.
翻译:持续学习的方法有助于深层神经网络模型的适应,并通过试图解决灾难性的遗忘而逐步学习。然而,这些现有方法传统上适用于基于图像的任务,对移动或嵌入式遥感系统产生的连续时间序列数据是否同样有效,这仍然是一个没有答案的问题。为解决这一空白,我们进行了第一次全面的经验研究,对三种主要的持续学习方案(即正规化、重放和重现实例)的绩效进行了量化,这些系统包括三个移动和嵌入式遥感应用的6个数据集,这些应用在一系列具有不同学习复杂性的情景中。更具体地说,我们在边缘设备上实施端到端的持续学习框架。然后我们调查性能、性能、存储、计算成本和不同持续学习方法的记忆足迹之间的一般性、交易性、不同持续时间序列。我们的研究结果表明,在像 iCaRL 这样的基于Explamerical的系统,其最佳的绩效交换,即使是在复杂的情景中,可以牺牲某些存储空间(freial-MBs)用于持续学习模式(1%至5%)。我们第一次在边缘设备上实施一个端的端端端端持续学习框架。我们还显示,现在可以实际操作在持续的固定和不断学习模式中学习两个阶段的数据。