Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired while non-defective samples come almost for free. The defects however often seem to resemble each other, e.g., scratches on different products may only differ in a few characteristics. In this work, we introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent defect types independent of and across various background products and yet can apply defect-specific styles to generate realistic defective images. An empirical study on the MVTec AD and two additional datasets showcase DT-GAN outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and diversity in defect generation. We further demonstrate benefits for a critical downstream task in manufacturing -- defect classification. Results show that the augmented data from DT-GAN provides consistent gains even in the few samples regime and reduces the error rate up to 51% compared to both traditional and advanced data augmentation methods.

翻译：数据匮乏与数据不平衡是许多深度学习方法的两个主要难题。例如，在高度优化的生产线上，难以获取缺陷样本，而合格样本几乎可以不计成本地获取。然而，不同缺陷往往具有相似性，例如不同产品上的划痕可能仅在某些特征上存在差异。本文提出一种名为缺陷转移生成对抗网络（DT-GAN）的框架，该框架能学习独立于各类背景产品且跨产品通用的缺陷类型表征，同时可施加缺陷特定风格以生成逼真的缺陷图像。在MVTec AD数据集及另外两个数据集上的实验研究表明，在缺陷生成的样本保真度和多样性方面，DT-GAN优于最先进的图像合成方法。我们进一步论证了该方法在制造业关键下游任务——缺陷分类中的优势。结果表明，即便在极少量样本场景下，DT-GAN生成的增强数据也能带来一致的性能提升，相较于传统及先进的数据增强方法，错误率最高可降低51%。