There are 2 repositories under celeba-dataset topic.
A Collection of Variational Autoencoders (VAE) in PyTorch.
[CVPR 2021] A large-scale face image dataset that allows text-to-image generation, text-guided image manipulation, sketch-to-image generation, GANs for face generation and editing, image caption, and VQA
Variational auto-encoder trained on celebA . All rights reserved.
Get started with CelebA-HQ dataset in under 5 mins !
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
Using Capsule Networks in GANS to generate very realistic fake images that could perhaps be used for deepfakes
Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)
Example of vanilla VAE for face image generation at resolution 128x128 using pytorch.
👦 Human head semantic segmentation
Who is your doppelgänger and more with Keras face recognition
Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019
Generative Adversarial Networks in PyTorch
Official adversarial mixup resynthesis repository
Apply Thatcher illusion on a set of face photos
Conditional VAE in Tensorflow 2 | Conditional Image Generation | CelebA dataset
This repository is related to a project of the Introduction to Numerical Imaging (i.e, Introduction à l'Imagerie Numérique in French), given by the MVA Masters program at ENS-Paris Saclay. It was entirely build from scratch and contains code in PyTorch Lightning to train and then use a neural network for image classification. We used it to create a classifier allowing semantic attributes classification of faces with the dataset CelebA-HQ.
Code for the paper "Transfer Learning for Facial Attribute Prediction and Clustering" (iSCI 2019)
Trained an End-to-End model for deblurring of celebrity faces (CelebA).
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs (ACM CCS'21)
Keras implementation of Variation Autoencoder for face generation. Analysis of the distribution of the latent space of the VAE. Vector arithemtic in the latent space. Morphing between the faces. The model was trained on CelebA dataset
This repository is about different types of GANs in pytorch, their proper settings and training results
Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio
Conditional face generation experiments using GAN models on CelebA dataset.
Keras implementation of WGAN GP for face generation. The model is trained on CelebA dataset.
Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks
A Variational Autoencoder in PyTorch for the CelebA Dataset
Facial Attributes,Multi-task Learning
Wasserstein GAN with gradient penalty tutoria, PyTorch ver.
A model with CycleGAN architecture for translating black & white images into colorized images
PyTorch Implementation of DCGAN (on CelebA dataset)
Tensorflow implementation of StarGAN. Feature translation between images using Generative Adversarial Networks (GANs). It allows to modify a physical characteristic such as the hair color.