jionie / Generative_Adversarial_Networks_PyTorch

(PyTorch) Implementations of GAN, Improved GAN, DCGAN, LAPGAN, and InfoGAN in PyTorch

Generative Adversarial Networks in PyTorch

About

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

Presented in the group meeting of Machine Discovery and Social Network Mining Lab, National Taiwan University.

Content

• 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

Sampled from MNIST dataset:

Generated by DCGAN:

Generated by Improved GAN:

Generated by Info-GAN:

Usage

Clone the repo and use the virtualenv:

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: http://pytorch.org

References

• 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.