We introduce a new approach for estimating generative models through an adversarial process,
where two models are trained simultaneously: a generative model G that captures the data distribution, 
and a discriminative model D that estimates the probability that a sample originates from the training 
data rather than from G. The training objective for G is to maximize the likelihood of D making an error. 
This setup forms a minimax game between two players. In the space of arbitrary functions for G and D, 
a unique solution exists, where G recovers the true data distribution, and D outputs 1/2 everywhere. When 
both G and D are modeled by multilayer perceptrons, the entire system can be trained using backpropagation. 
No Markov chains or unrolled approximate inference networks are required during either training or sample 
generation. Experimental results validate the framework, showcasing its potential through both qualitative 
and quantitative assessments of the generated samples.