SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
It addresses the need for unified generative models in industrial anomaly detection, reducing reliance on multiple specialized models, though it is incremental in building on existing U-Net and text-prompt techniques.
The paper tackles the problem of generating diverse anomalies, normal products, and anomaly masks in industrial settings with limited data, achieving improvements such as +8.66% pixel-level AP for synthesis-based anomaly detection and +12.79% IoU for supervised segmentation models.
We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive research exists, most efforts either focus on specific tasks, i.e., anomalies or normal products only, or require separate models for each anomaly type. Consequently, prior methods either offer limited generative capability or depend on a vast array of anomaly-specific models. We demonstrate that U-Net's differentiated learning ability captures the distinct visual traits of slightly-varied normal products and diverse anomalies, enabling us to construct a unified model for all tasks. Specifically, we first introduce an Unbalanced Abnormal (UA) Text Prompt, comprising one normal token and multiple anomaly tokens. More importantly, our Decoupled Anomaly Alignment (DA) loss decouples anomaly attributes and binds them to distinct anomaly tokens of UA, enabling SeaS to create unseen anomalies by recombining these attributes. Furthermore, our Normal-image Alignment (NA) loss aligns the normal token to normal patterns, making generated normal products globally consistent and locally varied. Finally, SeaS produces accurate anomaly masks by fusing discriminative U-Net features with high-resolution VAE features. SeaS sets a new benchmark for industrial generation, significantly enhancing downstream applications, with average improvements of $+8.66\%$ pixel-level AP for synthesis-based AD approaches, $+1.10\%$ image-level AP for unsupervised AD methods, and $+12.79\%$ IoU for supervised segmentation models. Code is available at \href{https://github.com/HUST-SLOW/SeaS}{https://github.com/HUST-SLOW/SeaS}.