Generation images of chest x-ray using GAN

Using Generative Adversarial Networks to generate bird photos.

This repository contains a Convolutional GAN which generates faces based on the CelebFaces dataset.

a PyTorch implementation - handwriting number recognition with GAN model using MNIST Dataset

This work demonstrates the simple and straight forward way of using the DC-GAN architecture to obtain bright new and unique ’waifus’ the heart can desire.

A repository for codes and projects related to Generative Models including: Autoencoders, VAEs, GANs, RBM

PyTorch implementations of simple GANs for lightweight generative modeling of input distributions.

Web application to generate anime avatars

Generative adversarial network for creating images of handwritten digits by a neural network

Python implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) for generating realistic fake faces using PyTorch.

Tensorflow implementation of Conditional GAN trained on MNIST dataset

Implementation of GAN using TensorFlow's Keras.

Generate image inpainting with adversarial edge learning

Generate image inpainting with adversarial edge learning

Generate pokemons image with GAN

The project involves utilizing Generative Adversarial Networks (GANs) to generate fake MNIST images. GANs are a class of machine learning models consisting of two components: a generator and a discriminator., which are implemented here using PyTorch framework.

Text to Image synthesis using GAN

.A Generative Adversarial Network (GAN) is a deep learning architecture used to generate new data that resembles existing data. It consists of two neural networks, a generator and a discriminator, that are trained in competition with each other. The generator creates synthetic data, while the discriminator tries to distinguish between real.

ESRGAN