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Is there a connection between: "Adversarial Learning" (AL) and "Generative Adversarial Networks" (GANs)? Is it valid to say that GANs employ AL?

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From Wikipedia,

Adversarial machine learning is a machine learning technique that attempts to exploit models by taking advantage of obtainable model information and using it to create malicious attacks. The most common reason is to cause a malfunction in a machine learning model.

Adversarial machine learning is a technique used in machine learning to fool or misguide a model with malicious input. While adversarial machine learning can be used in a variety of applications, this technique is most commonly used to execute an attack or cause a malfunction in a machine learning system.

Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in order to generate new, synthetic instances of data that can pass for real data. They are used widely in image generation, video generation and voice generation.

Generative adversarial networks are based on a game-theoretic scenario in which the generator network must compete against an adversary. The generator network directly produces samples. Its adversary, the discriminator network, attempts to distinguish between samples drawn from the training data and samples drawn from the generator.

Page 699, Deep Learning, 2016.

From Machinelearningmastery

We can think of the generator as being like a counterfeiter, trying to make fake money, and the discriminator as being like police, trying to allow legitimate money and catch counterfeit money. To succeed in this game, the counterfeiter must learn to make money that is indistinguishable from genuine money, and the generator network must learn to create samples that are drawn from the same distribution as the training data.

NIPS 2016 Tutorial: Generative Adversarial Networks, 2016.

In this way, the two models are competing against each other, they are adversarial in the game theory sense, and are playing a zero-sum game.

Because the GAN framework can naturally be analyzed with the tools of game theory, we call GANs “adversarial”.

NIPS 2016 Tutorial: Generative Adversarial Networks, 2016.

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