Collection of generative models in Tensorflow

Collection of generative models in Pytorch version.

:fire: PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain. :fire: 图像翻译，条件GAN，AI绘画

Awesome Generative Adversarial Networks with tensorflow

All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN

cGAN-based Multi Organ Nuclei Segmentation

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

Pytorch implementation of pix2pix for various datasets.

Implementation of Conditional Generative Adversarial Networks in PyTorch

Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN

Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning

Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.

Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)

Repository for implementation of generative models with Tensorflow 1.x

GAN models with Anime.

cGAN-based Manga Colorization Using a Single Training Image.

여러가지 유명한 신경망 모델들을 제공합니다. (DCGAN, VAE, Resnet 등등)

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.

MalDataGen is an advanced Python framework for generating and evaluating synthetic tabular datasets using modern generative models, including diffusion and adversarial architectures.

Procedural 3D Terrain Generation using Generative Adversarial Networks

[ECCV 2024] Soft Prompt Generation for Domain Generalization

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.

MXNet Implementation of DCGAN, Conditional GAN, pix2pix

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.

GANs Implementations in Keras