This project implements a CycleGAN-based method for image dehazing on a paired dataset containing hazy images and their corresponding ground truth clear images.
-
Updated
May 28, 2024 - Python
This project implements a CycleGAN-based method for image dehazing on a paired dataset containing hazy images and their corresponding ground truth clear images.
🌱 SNE-RoadSeg in PyTorch, ECCV 2020 by Rui (Ranger) Fan & Hengli Wang, but now we have improved it.
Solutions for Advanced Image Analysis course assignments, featuring model designs for image summation and generation with MNIST, and style transfer using CycleGAN with MNIST and SVHN datasets.
TNNLS 2024 submission. VerDisGAN and HorDisGAN which control the variation degrees for generated samples
Implemented basic deep learning models using PyTorch
Implementation of MultiStain-CycleGAN
CycleGAN implementation in PyTorch
Pytorch implementation of Self Attentive Adversarial Stain Normalization (SAASN).
An easy-to-modify and easy-to-follow re-implementation of CycleGAN (cycle-consistent generative adversarial network) in PyTorch
ArtGan for transferring real Images to realism art style.
Scientific Guide AI notebooks is a collection of machine learning and deep learning notebooks prepared by Salem Messoud.
This repository deals with generating 'malign' synthetic samples from 'benign' samples using CycleGAN to mitigate class imbalance and detecting Melanoma using a new balanced skin lesion image dataset.
Using CycleGAN to swap Pokemon types
Bald-to-Hairy Translation Using CycleGAN
PyTorch implementations of Generative Adversarial Network series
Telegram Bot на aiogram, преобразующий входящие фотографии с помощью Style Transfer или CycleGAN
This is a PyTorch implementation of Cycle GAN from Scratch.
Pytorch pipeline for 3D image domain translation using Cycle-Generative-Adversarial-networks, without paired examples.
Implement of CycleGAN using pytorch 1.8.
Unofficial Pytorch implementation of CycleGAN for MNIST, USPS, SVHN, MNIST-M, and SyntheticDigits datasets.
Add a description, image, and links to the cyclegan-pytorch topic page so that developers can more easily learn about it.
To associate your repository with the cyclegan-pytorch topic, visit your repo's landing page and select "manage topics."