Train a DCGAN to properly approximate the normal (not Gaussian!) distribution of images of interest. With this, we have one generator that can map a latent space to normal images and a discriminator that can discriminate real and fake images.
Now, we have a trained DCGAN. Say, we have a new image that is unknown whether the image is normal or abnormal. How can we know if this image is normal or abnormal using the trained GAN? We can do the next.
Find the z that gives the most visually similar G(z) to the given image. How? Set up a…
One of the challenges in the study of generative adversarial networks is the instability of its training
Spectral Normalization is a novel weight normalization technique to stabilize the training of discriminator of GANs by enforcing Lipschitz constraint on the discriminator of GANs. It only requires tuning Lipschitz constant in order to produce satisfactory performance.
WGAN and WGAN-GP tried to address the problem — instability of training of GANs.
WGAN: by clipping weights of discriminator
WGAN-GP: by introducing gradient penalty on its loss function
However, even WGAN-GP cannot impose regularization on the entire function (discriminator) space outside of the supports of…