Optimizing supervised generative adversarial networks via latent space regularizations

Abstract

A method for image generation based on a Generative Adversarial Network (GAN) including a generator, a discriminator, and an encoder, wherein outputs of the generator are mapped, by the encoder, to a latent space adaptable to manipulate at least one characteristics of images generated by the GAN, the method including generating, by the encoder, a first encoding E(Y) of a target image Y and a second encoding E(G(Z)) of a generated image G(Z) corresponding to the target image Y, wherein the first and second encodings E(Y) and E(G(Z)) map Y and G(Z) to the latent space having a lower dimension than dimensionality of Y and G(Z), wherein the encoder is trained to minimize the differences between the first and second encodings E(Y) and E(G(Z)), and the generator is trained by using the first and second encodings E(Y) and E(G(Z)) as part of a loss function.