AI-Generated Content-Based Edge Learning for Fast and Efficient Few-Shot Defect Detection in IIoT

Abstract

Generative AI has garnered substantial attention due to the limited defect samples in the industrial Internet of Things (IIoT). However, addressing the challenge of few-shot defect detection in industrial edge networks remains a key issue. In this paper, we propose ABEL, a novel AI-generated content (AIGC)-based edge learning framework for fast and efficient few-shot defect detection. This framework facilitates fast few-shot defect detection by harnessing the capabilities of realistic sample synthesis and edge-based AIGC task execution. Specifically, we propose an energy-based model (EBM)-guided Langevin Markov chain Monte Carlo (L-MCMC) image generation algorithm, synthesizing high-resolution industrial defect samples for efficient few-shot defect detection. Then, we formulate a large-scale mixed cooperative-competitive AIGC computation offloading problem and propose an attention and memory-based multi-agent reinforcement learning (AMMARL) algorithm to ensure fast edge execution of heterogeneous defect samples generative tasks. Particularly, the challenges of partial observability and high-dimensional state space are addressed by introducing multi-head attention mechanisms and long-term memory modules. Comprehensive synthesis experiments are conducted utilizing real-world industrial datasets NEU-CLS and DeepPCB. The experimental results demonstrate the effectiveness of our framework and algorithm's effectiveness in efficiently synthesizing realistic industrial defect images and optimizing edge-based AIGC task execution.