We propose a generative adversarial network (GAN) framework for estimating generative models. GANs simultaneously train two models: a generator G that captures the data distribution, and a discriminator D that estimates the probability that a sample came from training data rather than G. The generator is trained to maximize the probability of D making a mistake. This framework is formulated as a minimax two-player game. In the continuous functions G and D, a unique Nash equilibrium exists where G recovers the training data distribution and D cannot distinguish real vs. fake samples. When G and D are multilayer perceptrons, training can be done with backpropagation. No Markov chains or unrolled inference networks are needed during training or generation. Experiments demonstrate the potential of GANs through qualitative and quantitative evaluation of generated samples.
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