SalilBhatnagarDE/GAN_Pytorch

This refers to the implementation of Generative Adversarial Networks (GANs) using the PyTorch library. GANs are deep learning models that consist of two neural networks, a generator and a discriminator, which work in a competitive manner to generate realistic synthetic data.

GAN_Pytorch

Generative adversarial network (GAN) are well-known deep generative models proposed by Ian Goodfellow that could be used for synthesising data. It consists of two components, a generator (G) network that learns the data distribution and generates new examples and a discriminator (D) network that distinguishes between real and fake examples i.e. examples generated by G.

In this repository, I will show you the implementation of a series of tasks related to GANs using MNIST / Fashion-MNIST datasets.

1. Implementation of GANs using standard fully connected neural network on MNIST

Go through the NN_GAN.ipynb file and play with the implementaion of wonderful generative network. As the file is commented appropiately, you could easily follow through the implementation.

Results after training :

2. Implementation of Conditional GANs using CNN based networks on MNIST

Go through the Conditional_GAN.ipynb file and play with the implementaion of wonderful generative network. As the file is commented appropiately, you could easily follow through the implementation.

Results after training :