Generative Adversarial Networks (1406.2661v1)

Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

• The paper introduces a minimax game between a generative and a discriminative model to iteratively enhance the realism of generated samples.
• It circumvents traditional probabilistic methods by eliminating the need for complex Markov chain Monte Carlo processes during training.
• Experimental results demonstrate the framework's capability to produce indistinguishable counterfeits across diverse datasets, paving the way for advanced applications.

Introduction

The paper introduces an innovative framework for training generative models, which are systems capable of producing new data samples that resemble a given data distribution. This framework establishes a competitive scenario where a generative model, functioning like counterfeiters trying to create fake data, is continuously challenged by a discriminative model, acting as the police, trying to distinguish real data from counterfeit samples. The approach allows the generative model to improve its capacity to replicate the original data distribution without the need for complex probabilistic computations or Markov chains both during training and during the generation of new samples.

Related Work

Prior studies in generative modeling have largely focused on different neural network architectures such as Restricted Boltzmann Machines, Deep Boltzmann Machines, and Deep Belief Networks, each coming with their own set of challenges, mostly concerning the intractability of certain computations and the need for Markov chain Monte Carlo methods. Other models like denoising auto-encoders and noise-contrastive estimation also avoid exact likelihood computation but they encounter different limitations. The paper notes that most contemporary models are hindered by their complex inference requirements or constraints in generating diverse samples proficiently.

Adversarial Nets Framework

The proposed framework presents a system where two models, a generative model (G) and a discriminative model (D), are simultaneously trained through a minimax game. The generator creates samples from noise, aiming to mimic the authentic data distribution, while the discriminator evaluates samples to determine their origin—real or synthetic. This training process optimizes both models and, given enough time, allows the generator to produce samples indistinguishable from actual data, as judged by the discriminator. The experiments conducted demonstrate the framework's potential through the generation of realistic samples across multiple datasets.

Experiments, Results, and Conclusion

The framework was tested on various datasets and demonstrated the capability to generate convincing counterfeit samples that have been judged competitive with those produced by other generative models. The experiments validated the framework's effectiveness without the necessity for Markov chain Monte Carlo processes, a major advantage over previous methods. In summary, this framework introduces a compelling alternative approach for estimating generative models with the potential for various extensions, including conditional models, semi-supervised learning, and more efficient training strategies. The research paves the way for future developments that can enhance the capabilities and efficiency of generative adversarial networks.