The paper introduces a new framework for estimating generative models using an adversarial process, involving two models: a generative model \( G \) that captures the data distribution and a discriminative model \( D \) that estimates the probability of a sample coming from the training data rather than \( G \). The training procedure for \( G \) is to maximize the probability of \( D \) making mistakes, forming a minimax two-player game. In the space of arbitrary functions, a unique solution exists where \( G \) recovers the training data distribution and \( D \) is constant. For multilayer perceptrons, the system can be trained with backpropagation, eliminating the need for Markov chains or approximate inference networks. Experiments demonstrate the framework's potential through qualitative and quantitative evaluations of generated samples. The paper also discusses related work and provides theoretical analysis, showing that the minimax game has a global optimum when \( p_g = p_{\text{data}} \). Practical training algorithms are presented, and experiments on various datasets show competitive results. The framework offers advantages such as avoiding Markov chains and leveraging piecewise linear units, but requires careful synchronization between \( G \) and \( D \) during training. Future work includes extensions to conditional models, approximate inference, semi-supervised learning, and efficiency improvements.The paper introduces a new framework for estimating generative models using an adversarial process, involving two models: a generative model \( G \) that captures the data distribution and a discriminative model \( D \) that estimates the probability of a sample coming from the training data rather than \( G \). The training procedure for \( G \) is to maximize the probability of \( D \) making mistakes, forming a minimax two-player game. In the space of arbitrary functions, a unique solution exists where \( G \) recovers the training data distribution and \( D \) is constant. For multilayer perceptrons, the system can be trained with backpropagation, eliminating the need for Markov chains or approximate inference networks. Experiments demonstrate the framework's potential through qualitative and quantitative evaluations of generated samples. The paper also discusses related work and provides theoretical analysis, showing that the minimax game has a global optimum when \( p_g = p_{\text{data}} \). Practical training algorithms are presented, and experiments on various datasets show competitive results. The framework offers advantages such as avoiding Markov chains and leveraging piecewise linear units, but requires careful synchronization between \( G \) and \( D \) during training. Future work includes extensions to conditional models, approximate inference, semi-supervised learning, and efficiency improvements.