Image domain conversion using CycleGAN

This example shows how to convert images from one domain into another using CycleGAN

CycleGAN is a GAN model that is generally used for the following purposes.

Style transfer (images and paintings)
Season conversion
Day / night conversion
Object transformation

The difference from Pix2Pix, which also perform image-image conversion, is that CycleGAN uses unsupervised learning, so there is no need for a paired image dataset.
In this example, even with unsupervised learning, you can see the model convert the images by understanding whether the fruit was a whole one or a cut one.

result image

Requirements

MATLAB version should be R2019b and later

Usage

The repository provides the following files:

CycleGANExample.mlx — Example showing how to train the CycleGAN model
generator.m — Function to create a CycleGAN generator network
discriminator.m — Function to create a CycleGAN discriminator network
cycleGanImageDatastore.m — Datastore to prepare batches of images for training
cycleGAN_1000.mat - Pretrained model that converts apples to oranges and vice-versa

To run, open CycleGANExample.mlx and run the script. You can train the model or use the pretrained model by setting the doTraining flag to false.

Reference

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
(Jun-Yan Zhu.etc, 2017)