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Generative Adversarial Networks implemented in PyTorch and Tensorflow
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
Generating faces using DCGANs
Generated never before seen faces from a dataset of Celebrity Faces
Nike Shoes Generation with Deep Convolutional Generative Adversarial Networks
This repo implements DCGAN model and trains it on MNIST and Celeb Faces dataset
Build a Deep Convolutional Generative Adversarial Networks (DCGANs) to generate new images of faces.
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 dataset. The generated images can then be used to build a classifier that can identify an image as real vs fake. GANs like a DCGAN have been used widely to create Deep Fakes. The aim is to use DCGAN to build high-quality deep fakes.
Gradually building generative adversarial networks
This project is about implementing GANs on a celebrity face dataset in Kaggle and using DCGANs to generate realistic faces