Optimizing supervised generative adversarial networks via latent space regularizations

A method for image generation based on a Generative Adversarial Network (GAN), the GAN comprising 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 comprising:

• 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 generated image G(Z) corresponding to the target image Y is generated by the generator, the first encoding E(Y) and the second encoding E(G(Z)) map the target image Y and the generated image G(Z) to the latent space having a lower dimension than dimensionality of the target image Y and the generated image G(Z), and a first loss function between the first encoding E(Y) and the second encoding E(G(Z)) is minimized for the encoder;
• and
• training the generator using the first encoding E(Y) and the second encoding E(G(Z)) as part of a second loss function for the generator.

A method for image generation based on a Generative Adversarial Network (GAN), the GAN comprising 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 comprising:

• 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 generated image G(Z) corresponding to the target image Y is generated by the generator, and the first and second encodings E(Y) and E(G(Z)) map the target image Y and the generated image G(Z) to the latent space having a lower dimension than dimensionality of the target image Y and the generated image 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 of the generator.