# Generative Adversarial Nets

@inproceedings{Goodfellow2014GenerativeAN, title={Generative Adversarial Nets}, author={Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron C. Courville and Yoshua Bengio}, booktitle={NIPS}, year={2014} }

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. [] Key Result Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of…

## 34,122 Citations

### Probabilistic Generative Adversarial Networks

- Computer ScienceArXiv
- 2017

The central idea is to integrate a probabilistic model (a Gaussian Mixture Model) into the GAN framework which supports a new kind of loss function (based on likelihood rather than classification loss), and at the same time gives a meaningful measure of the quality of the outputs generated by the network.

### Adaptive Density Estimation for Generative Models

- Computer ScienceNeurIPS
- 2019

This work shows that their model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models and improved likelihood scores.

### Analyzing and Improving Adversarial Training for Generative Modeling

- Computer Science
- 2022

This AT generative model achieves competitive image generation performance to state-of-the-art EBMs, and at the same time is stable to train and has better sampling efficiency and is well-suited for the task of image translation and worst-case out- of-distribution detection.

### Generative Adversarial Nets: Can we generate a new dataset based on only one training set?

- Computer ScienceArXiv
- 2022

This work aims to generate a new dataset that has a different distribution from the training set, and finds the Jensen-Shannon divergence between the distributions of the generative and training datasets can be controlled by some target δ ∈ [0 , 1] .

### Hierarchical Mixtures of Generators for Adversarial Learning

- Computer Science2020 25th International Conference on Pattern Recognition (ICPR)
- 2021

This work proposes the hierarchical mixture of generators, inspired from the hierarchical mix of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves, just like the original GAN model.

### A Framework of Composite Functional Gradient Methods for Generative Adversarial Models

- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2021

The theory shows that with a strong discriminator, a good generator can be obtained by composite functional gradient learning, so that several distance measures between the probability distributions of real data and generated data are simultaneously improved after each functional gradient step until converging to zero.

### Imitating Generative Adversarial Networks with Humans

- Computer Science
- 2018

Experiments demonstrate that humans can converge in performance with a small set of queries and show potential for systems in which one or both components of a GAN can be replaced by humans.

### AdaGAN: Boosting Generative Models

- Computer ScienceNIPS
- 2017

An iterative procedure, called AdaGAN, is proposed, where at every step the authors add a new component into a mixture model by running a GAN algorithm on a re-weighted sample by inspired by boosting algorithms.

### Inverting the Generator of a Generative Adversarial Network

- Computer ScienceIEEE Transactions on Neural Networks and Learning Systems
- 2019

This paper introduces a technique, inversion, to project data samples, specifically images, to the latent space using a pretrained GAN, and demonstrates how the proposed inversion technique may be used to quantitatively compare the performance of various GAN models trained on three image data sets.

### Partially Conditioned Generative Adversarial Networks

- Computer ScienceArXiv
- 2020

This work argues that standard Conditional GANs are not suitable for such a task and proposes a new Adversarial Network architecture and training strategy to deal with the ensuing problems and demonstrates the value of the proposed approach in digit and face image synthesis under partial conditioning information.

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