Enhancing Underwater Vision: GANs & Transformers for Dehazing and Object Detection

Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

PyTorch implementation of the Cycle GAN paper.

YNR is a CycleGan model I shortened from "You're not real". It's an AI model I built to erase humans from a frame.

A simple CycleGAN for style transfer on a dataset of emojis

The repository contains the code and datasets for Arabic Calligraphy generation task.

This repository contains the code for the paper "Self-supervised Text Style Transfer using Cycle-Consistent Adversarial Networks".

Implement Cycle GAN using PyTorch from scratch

A (clean) PyTorch implementation of CycleGAN on Horse2zebra dataset

writing style transfer using cycle gan

Unpaired Image-to-Image Translation using CycleGANs (horse2zebra)

Night Augmentation almost based on GAN

Official Implementation for the paper Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking System.

A deep learning model to age faces in the wild, currently runs at 60+ fps on GPUs

We will visualize the style transfer output produced by monet_generator_model. We take 5 sample images that are photos of beautiful landscapes in the original dataset and feed them to the model.

🖼️ Our CycleGAN Implementation for Image-to-Image Translation project leverages PyTorch to seamlessly transform images between domains, all without paired examples. With a keen focus on innovation and effectiveness, we've explored CycleGAN's capabilities across various domains. Join us as we delve into the world of image translation technology! 🚀

Advanced Study of VAEs and GANs using a Colored MNIST Dataset.

Gender Change of faces implemented with CycleGAN (in Keras) for Deep Learning Course

Converting night into day is one of the most interesting applications in generative models, due to the great difficulty in recreating the scene during the day, especially in cases of extreme darkness, and thus the difficulty lies in imagining the scene during the day when the lighting is very weak.