Generative Adversarial Networks implemented in PyTorch and Tensorflow

This repository contains code and bonus content which will be added from time to time for the books "Learning Generative Adversarial Network- GAN" and "R Data Analysis Cookbook - 2nd Edition" by Packt

Implementation of generative models

In this script, we use Deep Convolutional Generative Adversarial Networks (DCGANs) to generate new images that resemble CIFAR10 dataset images.

Use conditional-dcgans to generate realistic images of digits

Implementation of Capsule Network architecture in GANs using MNIST dataset

This repo implements DCGAN model and trains it on MNIST and Celeb Faces dataset

Nike Shoes Generation with Deep Convolutional Generative Adversarial Networks

As the crime rates have increased due to fake images and videos, it has become the need of the hour today to build technologies that could identify these threats and protect us from any potential scams. This project aims to implement a DCGAN (Deep Convolutional Generative Adversarial Network) to generate "fake" images based on an existing datase…

Generated never before seen faces from a dataset of Celebrity Faces

A Deep Convolutional Generative Adversarial Network (DCGAN) is an extension of the standard GAN architecture that uses deep convolutional networks for both the generator and discriminator models.

Build a Deep Convolutional Generative Adversarial Networks (DCGANs) to generate new images of faces.

This project is about implementing GANs on a celebrity face dataset in Kaggle and using DCGANs to generate realistic faces

A keras implementation of DCGAN to generate Pokèmon sprites.

This project implements a Cartoon Image Generator using Deep Convolutional GANs (DCGANs) to create realistic cartoon images through adversarial training.

Gradually building generative adversarial networks

Simple DCGAN

Comparative study of DCGAN vs Conditional GAN on low-resource settings with visualizations including loss curves, image quality, heatmaps, PCA, t-SNE, and performance metrics.