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Generative Adversarial Networks
Following are a few more examples; you can see the translation of seasons (summer
→ winter), photo → painting and vice versa, and horses → zebras and vice versa:
Figure 6: Further examples of CycleGAN translations
Later in the chapter we will also explore a TensorFlow implementation of
CycleGANs. Next we talk about the InfoGAN, a conditional GAN where the GAN
not only generates an image, but you also have a control variable to control the
images generated.
InfoGAN
The GAN architectures that we have considered up to now provide us with little
or no control over the generated images. The InfoGAN changes this; it provides
control over various attributes of the images generated. The InfoGAN uses the
concepts from information theory such that the noise term is transformed into latent
code that provides predictable and systematic control over the output.
The generator in an InfoGAN takes two inputs: the latent space Z and a latent code c,
thus the output of the generator is G(Z,c). The GAN is trained such that it maximizes
the mutual information between the latent code c and the generated image G(Z,c).
The following figure shows the architecture of the InfoGAN:
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