cgan
There are 187 repositories under cgan topic.
hwalsuklee/tensorflow-generative-model-collections
Collection of generative models in Tensorflow
znxlwm/pytorch-generative-model-collections
Collection of generative models in Pytorch version.
youyuge34/PI-REC
:fire: PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain. :fire: 图像翻译，条件GAN，AI绘画
kozistr/Awesome-GANs
Awesome Generative Adversarial Networks with tensorflow
hollobit/All-About-the-GAN
All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
kwotsin/mimicry
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
znxlwm/pytorch-MNIST-CelebA-cGAN-cDCGAN
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
alohaleonardo/Super_Resolution_with_CNNs_and_GANs
Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch
mafda/generative_adversarial_networks_101
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
zcemycl/Matlab-GAN
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
mahmoodlab/NucleiSegmentation
cGAN-based Multi Organ Nuclei Segmentation
znxlwm/tensorflow-MNIST-cGAN-cDCGAN
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
znxlwm/pytorch-pix2pix
Pytorch implementation of pix2pix for various datasets.
malzantot/Pytorch-conditional-GANs
Implementation of Conditional Generative Adversarial Networks in PyTorch
YudiDong/Channel_Estimation_cGAN
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
Panagiotou/ImageToDEM
Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning
wangguanan/Pytorch-Basic-GANs
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
ruiliu-ai/DivCo
Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)
Kyushik/Generative-Model
Repository for implementation of generative models with Tensorflow 1.x
FangYang970206/Anime_GAN
GAN models with Anime.
jk96491/Advanced_Models
여러가지 유명한 신경망 모델들을 제공합니다. (DCGAN, VAE, Resnet 등등)
sudheerachary/Manga_Colorization
cGAN-based Manga Colorization Using a Single Training Image.
ghunkins/Voice-Denoising-AN
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.
Panagiotou/Procedural3DTerrain
Procedural 3D Terrain Generation using Generative Adversarial Networks
MingtaoGuo/Residual-Dense-Network-Trained-with-cGAN-for-Super-Resolution
This repository is as a research project in the field of super resolution. It uses RDN as the generator and spectral norm is used in discriminator.
renytek13/Soft-Prompt-Generation
[ECCV 2024] Soft Prompt Generation for Domain Generalization
bowenc0221/MXNet-GAN
MXNet Implementation of DCGAN, Conditional GAN, pix2pix
germain-hug/GANs-Keras
GANs Implementations in Keras
s-chh/PyTorch-GANs
From scratch, simple and easy-to-understand Pytorch implementation of various generative adversarial network (GAN): GAN, DCGAN, Conditional GAN (cGAN), WGAN, WGAN-GP, CycleGAN, LSGAN, and StarGAN.
sian-chen/PyTorch-ECGAN
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".
vsooda/mxnet-wgan
mxnet implement for Conditional Wasserstein GAN
balansky/pytorch_gan
Spectral Normalization and Projection Discriminator
neuralchen/SNGAN_Projection
An unofficial PyTorch implementation of SNGAN (ICLR 2018) and cGANs with projection discriminator (ICLR 2018)
vnyk/DressGAN
Dress styles generation using GANs using TensorFlow
SBSeg25/MalDataGen
SynDataGen is an advanced Python framework for generating and evaluating synthetic tabular datasets using modern generative models, including diffusion and adversarial architectures.