Data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task

One-class defect detection has proven to be an effective technique. However, the performance of complex models is often limited by existing data augmentation methods. To address this issue, this paper proposes a novel data augmentation method based on a denoising diffusion probability model. This approach generates high-quality image samples using partial noise diffusion, eliminating the need for extensive training on large-scale datasets. Experimental results demonstrate that the proposed method outperforms current methods in one-class defect detection tasks. The proposed method offers a new perspective on data augmentation and demonstrates its potential to tackle challenging computer vision problems.