Wednesday, July 6, 2016

Generative Adversial Networks Explained

To gain some intuition, think of a back-and-forth situation between a bank and a money counterfeiter. At the beginning, the fakes are easy to spot. However, as the counterfeiter keeps trying different kinds of techniques, some may get past the check. The counterfeiter then can improve his fakes towards the areas that got past the bank's security checks.

But the bank doesn't give up. It also keeps learning how to tell the fakes apart from real money. After a long period of back-and-forth, the competition has led the money counterfeiter to create perfect replicas.

Now, take that same situation, but let the money forger have a spy in the bank that reports back how the bank is telling fakes apart from real money.

Every time the bank comes up with a new strategy to tell apart fakes, such as using ultraviolet light, the counterfeiter knows exactly what to do to bypass it, such as replacing the material with ultraviolet marked cloth.

The second situation is essentially what a generative adversial network does. The bank is known as a discriminator network, and in the case of images, is a convolutional neural network that assigns a probability that an image is real and not fake.

The counterfeiter is known as the generative network, and is a special kind of convolutional network that uses transpose convolutions, sometimes known as a deconvolutional network. This generative network takes in some 100 parameters of noise (sometimes known as the code) , and outputs an image accordingly.

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