A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
This work addresses the challenge of limited defective data for industrial anomaly detection, offering an incremental improvement over existing methods.
The paper tackles the problem of scarce defective samples for industrial anomaly detection by introducing a blended latent diffusion model with online adaptation to generate synthetic defective samples, resulting in state-of-the-art performance improvements of 1.5%, 1.9%, and 3.1% on MVTec AD metrics.
Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts. This paper introduces a novel algorithm designed to augment defective samples, thereby enhancing AD performance. The proposed method tailors the blended latent diffusion model for defect sample generation, employing a diffusion model to generate defective samples in the latent space. A feature editing process, controlled by a ``trimap" mask and text prompts, refines the generated samples. The image generation inference process is structured into three stages: a free diffusion stage, an editing diffusion stage, and an online decoder adaptation stage. This sophisticated inference strategy yields high-quality synthetic defective samples with diverse pattern variations, leading to significantly improved AD accuracies based on the augmented training set. Specifically, on the widely recognized MVTec AD dataset, the proposed method elevates the state-of-the-art (SOTA) performance of AD with augmented data by 1.5%, 1.9%, and 3.1% for AD metrics AP, IAP, and IAP90, respectively. The implementation code of this work can be found at the GitHub repository https://github.com/GrandpaXun242/AdaBLDM.git