  Visual surface inspection is a challenging task owing to the highly diverse
appearance of target surfaces and defective regions. Previous attempts heavily
rely on vast quantities of training examples with manual annotation. However,
in some practical cases, it is difficult to obtain a large number of samples
for inspection. To combat it, we propose a hierarchical texture-perceiving
generative adversarial network (HTP-GAN) that is learned from the one-shot
normal image in an unsupervised scheme. Specifically, the HTP-GAN contains a
pyramid of convolutional GANs that can capture the global structure and
fine-grained representation of an image simultaneously. This innovation helps
distinguishing defective surface regions from normal ones. In addition, in the
discriminator, a texture-perceiving module is devised to capture the spatially
invariant representation of normal image via directional convolutions, making
it more sensitive to defective areas. Experiments on a variety of datasets
consistently demonstrate the effectiveness of our method.
