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)
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.
Generative Adversarial Networks in PyTorch
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
DCGAN implementation using PyTorch
Simple Tensorflow implementation of StarGAN (CVPR 2018 Oral)
Deep Learning for Computer Vision 2018 Spring
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
Facial Attributes,Multi-task Learning
Example of vanilla VAE for face image generation at resolution 128x128 using pytorch.
PyTorch Implementation of DCGAN (on CelebA dataset)
A system for altering facial expressions in images using a Variational Autoencoder
Implementation of Perceptual Generative Autoencoders in PyTorch
Official adversarial mixup resynthesis repository
PyTorch implementation of image inpainting technique as proposed in paper "Sementic Image Inpainting with Deep Generative Modes by R.A. Yeh et al."
Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)
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