InfoGAN - Generative Adversarial Networks Part III

In Part I the original GAN paper was presented. Part II gave an overview of DCGAN, which greatly improved the performance and stability of GANs. In this final part, the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome. As seen in the examples of Part II, one can do interesting and impressive things when doing arithmetic on the noise vector of the generator. In the example below from the DCGAN paper, the input noise vectors of men with glasses are manipulated to give vectors that result in women with sunglasses once fed into the generator.