Continual Learning with Diffusion-based Generative Replay for Industrial Streaming Data
It addresses data drift challenges for industrial streaming data applications, representing an incremental advancement in continual learning methods.
This paper tackles the problem of data drift in Industrial Internet of Things (IIoT) environments by proposing a Continual Learning approach called Distillation-based Self-Guidance (DSG), which uses a diffusion-based generative replay mechanism to mitigate catastrophic forgetting, resulting in accuracy improvements of 2.9% to 5.0% over state-of-the-art baselines on datasets like CWRU, DSA, and WISDM.
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.