Trained a Variational AutoEncoder Model to reconstruct the image and then sampled the latent space in order to get newly generated face images

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."

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

Face Generation using Adversarial Models

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

Vision&Perception project using GANs with CelebA dataset

Face generation with DCGAN and SNGAN on CelebA dataset

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.

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

Repo containing basic and advanced GAN implementations.

Use GANs with normalization techniques like dropouts, batch normalization along with having a low variance in kernel weight initialization, achieve realistic images of faces trained on the CelebA dataset. Images also have been generated of hand written digits after being trained on the MNIST dataset. This would be useful for generating training …

Detects and extracts faces from the images from your gallery 👱‍♂️

Facial Attribute Recognition using the Transformer architecture, 91% on CelebA

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

Progressive GAN on celebrity faces