yuxin-jiang/Awesome-Anomaly-Generation
This repository provides a hierarchical taxonomy of key paperson anomaly generation methods, surpassing flat lists with fine-grained subcategories that delineate emerging hotspots
This resource helps professionals create realistic artificial anomalous data to address the scarcity of real-world defects in various domains. It offers a structured overview of methods like CutPaste, GAN, and Diffusion for generating synthetic images and masks. Anyone involved in quality control, medical imaging, or autonomous driving who needs more defect data for training and evaluating AI models would find this valuable.
Use this if you need to generate diverse, controllable synthetic defect samples to augment limited real-world anomaly data for improving your anomaly detection models.
Not ideal if you are looking for a software tool or an implementation to directly generate anomalies, as this is a curated list of research papers and methods.
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Dec 12, 2025
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