A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" - (EASY to READ)
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Updated
May 23, 2017 - Python
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" - (EASY to READ)
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.
Using generative adversarial networks to generate new images of faces (datasets: MNIST, CelebA).
Variational auto-encoder trained on celebA . All rights reserved.
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.
Trained an End-to-End model for deblurring of celebrity faces (CelebA).
Generative Adversarial Networks in PyTorch
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
GAN's, VAE's, AE's..............
DCGAN implementation using PyTorch
Simple Tensorflow implementation of StarGAN (CVPR 2018 Oral)
Deep Learning for Computer Vision 2018 Spring
Face Images generated using Deep Convolutional Generative Adversarial Networks
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 …
Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio
Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN) of non-cuda user s and its also used by cuda user.
Feature selection | Neural Network with feedforward propagation and back propagation
Face Generation using Adversarial Models
Facial Attributes,Multi-task Learning
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