Generative Adversarial Networks
This repository contains implementation of various architectures of Generative Models.
Implemented architectures
- GANs
- Wasserstein GANs
- WGAN with gradient penalty
- StyleGAN
Prerequisites
- Pytorch
- Numpy
Usage of GPU is highly recommended.
Datasets used
References
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks by Soumith et al.
- Generative Adversarial Networks by Ian Goodfellow et al.
- Wasserstein GAN by Soumith et al.
- Improved training of Wasserstein GANs by Arjovsky et al.
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks by Zhu et al.