Conclusion

In this chapter, we've discussed how to disentangle the latent representations of GANs. Earlier on in the chapter, we discussed how InfoGAN maximizes the mutual information in order to force the generator to learn disentangled latent vectors. In the MNIST dataset example, InfoGAN uses three representations and a noise code as inputs. The noise represents the rest of the attributes in the form of an entangled representation. StackedGAN approaches the problem in a different way. It uses a stack of encoder-GANs to learn how to synthesize fake features and images. The encoder is first trained to provide a dataset of features. Then, the encoder-GANs are trained jointly to learn how to use the noise code to control attributes of the generator output.

In the next chapter, we will embark on a new type of GAN that is able to generate new data in another domain. For example, given an image of a horse, the GAN can perform an automatic transformation to an image of a zebra. The interesting...