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The performances of defect inspection have been severely hindered byinsufficient defect images in industries, which can be alleviated by generatingmore samples as data augmentation. We propose the first defect image generationmethod in the challenging few-shot cases. Given just a handful of defect imagesand relatively more defect-free ones, our goal is to augment the dataset withnew defect images. Our method consists of two training stages. First, we traina data-efficient StyleGAN2 on defect-free images as the backbone. Second, weattach defect-aware residual blocks to the backbone, which learn to producereasonable defect masks and accordingly manipulate the features within themasked regions by training the added modules on limited defect images.Extensive experiments on MVTec AD dataset not only validate the effectivenessof our method in generating realistic and diverse defect images, but alsomanifest the benefits it brings to downstream defect inspection tasks. Codesare available at https://github.com/Ldhlwh/DFMGAN.

Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation