  Automated defect inspection is critical for effective and efficient
maintenance, repair, and operations in advanced manufacturing. On the other
hand, automated defect inspection is often constrained by the lack of defect
samples, especially when we adopt deep neural networks for this task. This
paper presents Defect-GAN, an automated defect synthesis network that generates
realistic and diverse defect samples for training accurate and robust defect
inspection networks. Defect-GAN learns through defacement and restoration
processes, where the defacement generates defects on normal surface images
while the restoration removes defects to generate normal images. It employs a
novel compositional layer-based architecture for generating realistic defects
within various image backgrounds with different textures and appearances. It
can also mimic the stochastic variations of defects and offer flexible control
over the locations and categories of the generated defects within the image
background. Extensive experiments show that Defect-GAN is capable of
synthesizing various defects with superior diversity and fidelity. In addition,
the synthesized defect samples demonstrate their effectiveness in training
better defect inspection networks.
