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Generative Adversarial Networks
The applications of GANs include creating images, videos, music, and even natural
languages. They have been employed in tasks like image-to-image translation, image
super resolution, drug discovery, and even next-frame prediction in video.
The key idea of GAN can be easily understood by considering it analogous to "art
forgery," which is the process of creating works of art that are falsely credited to
other usually more famous artists. GANs train two neural nets simultaneously. The
generator G(Z) is the one that makes the forgery, and the discriminator D(Y) is the
one that can judge how realistic the reproductions are, based on its observations of
authentic pieces of art and copies. D(Y) takes an input Y (for instance, an image), and
expresses a vote to judge how real the input is. In general, a value close to 1 denotes
"real," while a value close to 0 denotes "forgery." G(Z) takes an input from random
noise Z and it trains itself to fool D into thinking that whatever G(Z) produces is real.
The goal of training the discriminator D(Y) is to maximize D(Y) for every image
from the true data distribution, and to minimize D(Y) for every image not from
the true data distribution. So, G and D play opposite games: hence the name
adversarial training. Note that we train G and D in an alternating manner, where
each one of their objectives is expressed as a loss function optimized via a gradient
descent. The generative model continues to improve its forgery capabilities, and
the discriminative model continues to improve its forgery recognition capabilities.
The discriminator network (usually a standard convolutional neural network)
tries to classify if an input image is real or generated. The important new idea is
to backpropagate through both the discriminator and the generator to adjust the
generator's parameters in such a way that the generator can learn how to fool the
discriminator more often. At the end the generator will learn how to produce images
that are indistinguishable from the real ones:
Of course, GANs involve working towards an equilibrium in a game involving two
players. Let us first understand what we mean by equilibrium here. When we start,
one of the two players are hopefully better than the other. This pushes the other to
improve and this way both generator and discriminator push each other towards
improvement.
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