Write the abstract of the machine learning whitepaper "Generative Adversarial Nets".
An abstract for a generic paper about Generative Adversarial Networks could go as follow:

Generative Adversarial Networks (GANs) are a deep learning framework that involves training two neural networks, a generator, and a discriminator, in a game-theoretic manner. The generator generates new examples, while the discriminator evaluates the authenticity of these examples, trying to distinguish between real and fake data. The two models are trained in parallel, with the generator trying to produce examples that can fool the discriminator and the discriminator trying to identify the real examples from the generated ones correctly. Through this adversarial training, GANs can learn to generate highly realistic examples from a given dataset and have been successfully applied in a variety of domains, including image synthesis, data augmentation, and domain transfer. The present paper provides an overview of the key concepts and techniques in GANs, as well as some recent developments and challenges in this field.

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