Title: Linear Discriminant Generative Adversarial Networks,
Abstract: We develop a novel method for training of GANs for unsupervised and class
conditional generation of images, called Linear Discriminant GAN (LD-GAN). The
discriminator of an LD-GAN is trained to maximize the linear separability
between distributions of hidden representations of generated and targeted
samples, while the generator is updated based on the decision hyper-planes
computed by performing LDA over the hidden representations. LD-GAN provides a
concrete metric of separation capacity for the discriminator, and we
experimentally show that it is possible to stabilize the training of LD-GAN
simply by calibrating the update frequencies between generators and
discriminators in the unsupervised case, without employment of normalization
methods and constraints on weights. In the class conditional generation tasks,
the proposed method shows improved training stability together with better
generalization performance compared to WGAN that employs an auxiliary
classifier.