In the field of machine learning, a novel framework for estimating generative models is proposed. This framework, called "Generative Adversarial Nets" (GANs), uses an adversarial process to train two models: a generative model (G) that captures the data distribution, and a discriminative model (D) that estimates the probability of a sample being from the training data rather than G. The training procedure for G involves maximizing the probability of D making a mistake. The resulting minimax two-player game has a unique solution where G approximates the training data distribution and D is equal to 1/2 everywhere. The GAN framework can be implemented using multilayer perceptrons and trained with backpropagation, without the need for any Markov chains or unrolled approximate inference networks. Empirical evaluation of the generated samples showcases the potential of the proposed GANs framework.
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