Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation

Understand

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.