Generative Adversarial Nets

The main idea is to develop a generative model via an adversarial process. We will discuss what is an adversarial process later. GAN consists of two model. The one is generative model G and the other is discriminative model D. The purpose of a generative model is to generate the closest data as possible for give some input. The purpose of a discriminative model between two classes 0 and 1. 0 meaning the class belongs to Generative output and 1 meaning the class belongs to the true input sample from the original data.

This architecture corresponds to the minmax two-player game. One tries to create conflict over the other. Such networks are called adversarial networks. In the process of creating conflicts, both of them learn to be better and stronger than each other. When the discriminator makes an output of value ½ or 0.5, it implies that the discriminator is not able to distinguish whether the value came from the generator output or the original sample.

Here, the G and D are defined by the multilayered perceptron such that the entire system can be trained with back propagation. The training of the discriminator and generator are done separately.

According to the paper, the generative model can be thought of as analogous to a team of counterfeiters who are trying to produce a fake currency and use them without getting caught.

While, the discriminative model can be thought of as analogous to the Police who are trying to detect the fake currency. Here, both the teams try to improve their methods until the currencies are indistinguishable from the original currency.

Adversarial Networks

Straight from the paper,

To learn the generator’s distribution Pg over data x, we define a prior on input noise variables Pz(z), then represent a mapping to data space as G(z; θg ).

where G is a differentiable function represented by a multilayer perceptron with parameters θ g .

We also define a second multilayer perceptron D(x; θd ) that outputs a single scalar.

Where D(x) represents the probability that x came from the data rather than Pg.

The architecture of GAN can be explained from the following figure.

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