Title: cGANs with Projection Discriminator,
Abstract: We propose a novel, projection based way to incorporate the conditional
information into the discriminator of GANs that respects the role of the
conditional information in the underlining probabilistic model. This approach
is in contrast with most frameworks of conditional GANs used in application
today, which use the conditional information by concatenating the (embedded)
conditional vector to the feature vectors. With this modification, we were able
to significantly improve the quality of the class conditional image generation
on ILSVRC2012 (ImageNet) 1000-class image dataset from the current
state-of-the-art result, and we achieved this with a single pair of a
discriminator and a generator. We were also able to extend the application to
super-resolution and succeeded in producing highly discriminative
super-resolution images. This new structure also enabled high quality category
transformation based on parametric functional transformation of conditional
batch normalization layers in the generator.