Cyclic generative adversarial network for unsupervised cross-domain image generation

A computer-implemented method for unsupervised cross-domain image generation relative to a first image domain and a second image domain that each include real images, comprising:

• generating, by a first image generator, synthetic images having a similar appearance to one or more of the real images in the second image domain while including a semantic content of one or more of the real images in the first image domain;
• generating, by a second image generator, synthetic images having a similar appearance to at least one of the real images in the first image domain while including a semantic content of at least one of the real images in the second image domain;
• discriminating, by a first discriminator, the real images in the first image domain against the synthetic images generated by the second image generator;
• and
• discriminating, by a second discriminator, the real images in the second image domain against the synthetic images generated by the first image generator,
• wherein the discriminators and the generators are each neural network based and respectively form a generative network and a discriminative network in a cyclic Generative Adversarial Network (GAN) framework, and the method further comprises increasing an error rate of the discriminative network to improve a quality of the synthetic images.

A computer program product for unsupervised cross-domain image generation relative to a first image domain and a second image domain that each include real images, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• generating, by a first image generator of the computer, synthetic images having a similar appearance to one or more of the real images in the second image domain while including a semantic content of one or more of the real images in the first image domain;
• generating, by a second image generator of the computer, synthetic images having a similar appearance to at least one of the real images in the first image domain while including a semantic content of at least one of the real images in the second image domain;
• discriminating, by a first discriminator of the computer, the real images in the first image domain against the synthetic images generated by the second image generator;
• and
• discriminating, by a second discriminator of the computer, the real images in the second image domain against the synthetic images generated by the first image generator,
• wherein the discriminators and the generators are each neural network based and respectively form a generative network and a discriminative network in a cyclic Generative Adversarial Network (GAN) framework, and the method further comprises increasing an error rate of the discriminative network to improve a quality of the synthetic images.