Implementation of generative models
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.Wasserstein GANs.py.un~
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README.md

README.md

Generative Adversarial Networks

This repository contains implementation of various architectures of Generative Models.

Papers to read (Prerequisites)

Implemented architectures

  • GANs
  • Wasserstein GANs
  • WGAN with gradient penalty
  • CycleGANs (soon)

Prerequisites

Usage of GPU is highly recommended.

Datasets used

Cloning the Repository

$ git clone https://github.com/prajjwal1/gans

WGAN GP

To train the model:

$ cd WGAN
$ python wgan_gp.py

Note

This repository is under constant development. Will be updated regularly.