# gans

**Repository Path**: wang_zhi_andy/gans

## Basic Information

- **Project Name**: gans
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-28
- **Last Updated**: 2021-07-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

#  gans: Generative Adversarial Networks
Multiple Generative Adversarial Networks (GANs) implemented in PyTorch and Tensorflow.

[Check out this blog post](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f) for an introduction to Generative Networks. 


<img src=".images/dcgan_mnist.gif" width="275"> <img src=".images/dcgan_cifar.gif" width="275">

## Vanilla GANs
Vanilla GANs found in this project were developed based on the original paper [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) by Goodfellow et al.

These are trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), and learn to create hand-written digit images using a 1-Dimensional vector representation for 2D input images.
- [PyTorch Notebook](https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20PyTorch.ipynb)
- [TensorFlow Notebook](https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20TensorFlow.ipynb)

<img src=".images/vanilla_mnist_pt_raw.png" width="300"> <img src=".images/vanilla_mnist_pt.png" width="300">

__MNIST-like generated images before & after training.__


## DCGANs
Deep Convolutional Generative Adversarial Networks (DCGANs) in this repository were developed based on the original paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) by Radford et al.

These are trained on the [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) and the [MNIST](http://yann.lecun.com/exdb/mnist/) datasets. They use 3 dimensional representations for images (length x height x colors) directly for training.

- [TensorFlow CIFAR10 Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20TensorFlow.ipynb)
- [PyTorch CIFAR10 Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20PyTorch.ipynb)
- [PyTorch MNIST Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20PyTorch-MNIST.ipynb)

<img src=".images/dcgan_cifar_pt_raw.png" width="300"> <img src=".images/dcgan_cifar_pt.png" width="300">

__CIFAR-like generated images before & after training.__