How to Train Image Translation Models Without Pairs of Images? Unpaired Image Translation with CycleGAN

Discover the magic of CycleGAN and learn how to perform image translation without paired datasets using PyTorch.

Unpaired Image Translation with CycleGAN | Tutorial

Introduction

In the realm of image translation, traditional methods often rely on paired datasets — images that have a direct correspondence between source and target domains. But what if such paired data is hard to come by? Enter CycleGAN, a revolutionary approach that enables image translation without the need for paired datasets. In this blog post, we’ll dive deep into the implementation of CycleGAN using PyTorch, breaking down the code to understand how unpaired image translation is made possible.

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Overview of CycleGAN

CycleGAN, introduced by Zhu et al., addresses the problem of unpaired image-to-image translation using a cycle consistency loss. The key idea is to train two generator networks:

• G: Translates images from domain X to domain Y.