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Generative Adversarial Networks
The second term on the RHS is the adversarial loss. It is the standard generative loss
term, designed to ensure that images generated by the generator are able to fool the
discriminator. You can see in the following figure taken from the original paper that
the image generated by the SRGAN is much closer to the original high-resolution
image:
Another noteworthy architecture is CycleGAN; proposed in 2017, it can perform the
task of image translation. Once trained you can translate an image from one domain
to another domain. For example, when trained on a horse and zebra dataset, if you
give it an image with horses in the foreground, the CycleGAN can convert the horses
to zebra with the same background. We explore it next.
CycleGAN
Have you ever imagined how some scenery would look if Van Gogh or Manet
had painted it? We have many scenes and landscapes painted by Gogh/Manet, but
we do not have any collection of input-output pairs. A CycleGAN performs
the image translation, that is, transfers an image given in one domain (scenery
for example) to another domain (a Van Gogh painting of the same scene, for
instance) in the absence of training examples. The CycleGAN's ability to perform
image translation in the absence of training pairs is what makes it unique.
To achieve image translation the authors used a very simple and yet effective
procedure. They made use of two GANs, the generator of each GAN performing the
image translation from one domain to another.
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