面向工业流式数据的基于扩散的生成重放持续学习
Continual Learning with Diffusion-based Generative Replay for Industrial Streaming Data
AI总结：
针对工业物联网动态环境下的数据漂移及资源受限问题，提出基于蒸馏自引导的扩散生成重放持续学习方法DSG，通过知识蒸馏提升生成器稳定性与数据质量以缓解灾难性遗忘，在多个数据集上准确率较最优基线提升2.9%-5.0%。
摘要：
工业物联网（IIoT）整合互联传感器与设备以支撑工业应用，但其动态环境带来了数据漂移相关挑战。考虑到资源有限以及需让模型有效适配新数据分布的需求，本文提出一种持续学习（CL）方法——基于蒸馏的自引导（Distillation-based Self-Guidance, DSG），通过一种新型生成重放机制应对工业流式数据带来的挑战。DSG利用知识蒸馏将知识从先前的基于扩散的生成器迁移至更新后的生成器，既提升生成器的稳定性，也提高重现数据的质量，从而更好地缓解灾难性遗忘。在CWRU、DSA和WISDM数据集上的实验结果证明了DSG的有效性。DSG在准确率上优于当前最优基线，在关键数据集上实现了2.9%至5.0%的提升，展现出其在实际工业应用中的潜力。
英文摘要：
The Industrial Internet of Things (IIoT) integrates interconnected sensors and devices to support industrial applications, but its dynamic environments pose challenges related to data drift. Considering the limited resources and the need to effectively adapt models to new data distributions, this paper introduces a Continual Learning (CL) approach, i.e., Distillation-based Self-Guidance (DSG), to address challenges presented by industrial streaming data via a novel generative replay mechanism. DSG utilizes knowledge distillation to transfer knowledge from the previous diffusion-based generator to the updated one, improving both the stability of the generator and the quality of reproduced data, thereby enhancing the mitigation of catastrophic forgetting. Experimental results on CWRU, DSA, and WISDM datasets demonstrate the effectiveness of DSG. DSG outperforms the state-of-the-art baseline in accuracy, demonstrating improvements ranging from 2.9% to 5.0% on key datasets, showcasing its potential for practical industrial applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
