In this article, I’ll walk through the mathematics behind Cycle-Consistent Adversarial Networks. Please read the paper for a more comprehensive explanation.

CycleGAN is a method of unpaired image to image translation. Unfortunately, it’s possible to use CycleGAN without fully understanding or appreciating the mathematics involved. That is a real shame.

In this article, I’ll walk through the mathematics behind Cycle-Consistent Adversarial Networks. Please read the paper for a more comprehensive explanation.

The key thing with CycleGAN is that we don’t have before and after images.

Let’s take the example shown above of converting a zebra into a horse (and vice-versa).

In a paired dataset the horse and zebra need to “match” each other. We’re essentially taking a horse and painting it black and white. The background, lightning, etc. stays the same.

A paired dataset would look something like this:

Image by Jun-Yan Zhu on Github

In an unpaired dataset the horses and zebras don’t need to “match” each other. This is dataset is far easier to create.

loss-function cyclegan generative-adversarial deep-learning machine-learning

For the calculation of Loss, various optimization techniques are used in the field of Machine learning and Deep learning. This article will cover commonly used loss function in Machine learning and Deep learning, its use and mathematics behind it.

In this post, I’ll demonstrate the behavior of Generative Adversarial Networks (GANs) on 3D images and how it can help to generate novel 3D images.

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