# Generative_Adversarial_Networks_PyTorch
**Repository Path**: jiguo_li/Generative_Adversarial_Networks_PyTorch
## Basic Information
- **Project Name**: Generative_Adversarial_Networks_PyTorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-06
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
Generative Adversarial Networks in PyTorch
=======
[](https://travis-ci.org/AaronYALai/Generative_Adversarial_Networks_PyTorch)
[](https://coveralls.io/github/AaronYALai/Generative_Adversarial_Networks_PyTorch?branch=master)
About
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The repo is about the implementations of GAN, DCGAN, Improved GAN, LAPGAN, and InfoGAN in PyTorch.
My presentation about GANs' recent development (at 2017.01.17): [Presentation slides](https://docs.google.com/presentation/d/1HRNjCo_0PlspynoJKuoEF1AYkaKaUNgMzQ4nqiTlNUM/edit#slide=id.p)
Presented in the group meeting of Machine Discovery and Social Network Mining Lab, National Taiwan University.
Content
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- Generative Adversarial Nets (GAN): invented "adversarial nets" framework - a generative model G and a discriminative model D play a minimax two-player game.
- DC-GAN: proposed a set of constraints on the architectural topology of Convolutional GANs that make them stable to train in most settings.
- LAP-GAN: a cascade of generative models within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion (high-resolution).
- Improved GAN (minibatch discrimination): allow the discriminator to look at multiple data examples in combination by incorporating the closeness between examples in a minibatch as side information.
- Info-GAN: an information-theoretic modification to the objective of Generative Adversarial Network that encourages it to learn interpretable and disentangled representations.
Example
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#### Sampled from MNIST dataset:
#### Generated by DCGAN:
#### Generated by Improved GAN:
#### Generated by Info-GAN:
Usage
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Clone the repo and use the [virtualenv](http://www.virtualenv.org/):
git clone https://github.com/AaronYALai/Generative_Adversarial_Networks_PyTorch
cd Generative_Adversarial_Networks_PyTorch
virtualenv venv
source venv/bin/activate
Install pytorch and all dependencies and run the model (in Linux):
pip install https://download.pytorch.org/whl/cu75/torch-0.1.10.post2-cp27-none-linux_x86_64.whl
pip install torchvision
pip install -r requirements.txt
cd GAN
python run_GAN.py
More details about the installation about PyTorch:
References
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- GAN: I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” NIPS, 2014.
- DC-GAN: Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv 2015.
- LAP-GAN: Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks." NIPS 2015.
- Improved GAN: Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. “Improved techniques for training gans.” NIPS 2016.
- Info-GAN: Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. “Infogan: Interpretable representation learning by information maximizing generative adversarial nets.” NIPS 2016.