mxnet implement for Conditional Wasserstein GAN
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Updated
Apr 23, 2017 - Python
mxnet implement for Conditional Wasserstein GAN
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
Tensorflow implementation of pix2pix for various datasets.
Automatic Colorization of images using cGAN
GANs Implementations in Keras
A Conditional Generative Adverserial Network (cGAN) was adapted for the task of source de-noising of noisy voice auditory images. The base architecture is adapted from Pix2Pix.
Implementation of Conditional Generative Adversarial Networks in PyTorch
Implement multiple gan including vanilla_gan, dcgan, cgan, infogan and wgan with tensorflow and dataset including mnist.
Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch
GAN image generator with django (backend)
Here i present several GAN models in format of notebook implemented with tensorflow using the layers API
All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
Image-to-image translation with pix2pix
Spectral Normalization and Projection Discriminator
Deep learning classifier and image generator for building architecture.
C-GAN Demo for aerial image to map image translation
personal variation of https://github.com/sudheerachary/Manga_Colorization
Comic Colorization with cGAN
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