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arXiv · 2502.16412

A Survey on Industrial Anomalies Synthesis

Yanshu Wang·Xichen Xu·Jiaqi Liu·Xiaoning Lei·Guoyang Xie·Guannan Jiang·Zhichao Lu
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Abstract

This paper comprehensively reviews anomaly synthesis methodologies. Existing surveys focus on limited techniques, missing an overall field view and understanding method interconnections. In contrast, our study offers a unified review, covering about 40 representative methods across Hand-crafted, Distribution-hypothesis-based, Generative models (GM)-based, and Vision-language models (VLM)-based synthesis. We introduce the first industrial anomaly synthesis (IAS) taxonomy. Prior works lack formal classification or use simplistic taxonomies, hampering structured comparisons and trend identification. Our taxonomy provides a fine-grained framework reflecting methodological progress and practical implications, grounding future research. Furthermore, we explore cross-modality synthesis and large-scale VLM. Previous surveys overlooked multimodal data and VLM in anomaly synthesis, limiting insights into their advantages. Our survey analyzes their integration, benefits, challenges, and prospects, offering a roadmap to boost IAS with multimodal learning. More resources are available at https://github.com/M-3LAB/awesome-anomaly-synthesis.

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Yanshu Wang, Xichen Xu, Jiaqi Liu, Xiaoning Lei, Guoyang Xie, Guannan Jiang, Zhichao Lu. 2025-12-06. A Survey on Industrial Anomalies Synthesis. https://arxiv.org/abs/2502.16412

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