Face generation with DCGAN and SNGAN on CelebA dataset

In this project, I’ve used Generative Adversarial Networks (GANs) to generate new images of human faces from scratch, based on the neural networks being trained on real human faces. I used the MNIST dataset and CelebFaces Attributes (CelebA) dataset in this project.

DCGAN implementation using PyTorch

Implementation of Perceptual Generative Autoencoders in PyTorch

PyTorch implementation of image inpainting technique as proposed in paper "Sementic Image Inpainting with Deep Generative Modes by R.A. Yeh et al."

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" - (EASY to READ)

Vision&Perception project using GANs with CelebA dataset

Face Generation using Adversarial Models

Behavior Recognition Based Action Decision

GAN's, VAE's, AE's..............

My solution to the Udacity project of creating a DCGAN to create new face images based on the CelebA image database

This project demonstrates a GAN built with PyTorch, using a subset of 5000 CelebA images. It leverages Wasserstein GAN with Gradient Penalty (WGAN-GP) for facial image generation. The provided models are trained for 200 epochs, showcasing integration of techniques from key research papers. Deeper Networks and more Training can improve results.

A CNN used to fetch a person's identity based on their face.

This repository inclused several applications of extracting and classifying features from face images.

Reconstructing occluded facial images of celebrities using conditional generative adversarial network

Repo containing basic and advanced GAN implementations.

📷 This project presents functionality for applying medical masks to face images.

Progressive GAN on celebrity faces

This GitHub repository contains an implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) for image generation. With this project, you can generate stunning and realistic images using the power of deep learning.