What are GANs, and How do they Work?
A Layman’s Introduction to GANs for Data Scientists using PyTorch
Most of us in data science has seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. We’ve reached a stage where it’s becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. However, with the currently available machine learning toolkits, creating these images yourself is not as difficult as you might think.
In my view, GANs will change the way we generate video games and special effects. Using this approach, we could create realistic textures or characters on demand.
So in this post, we’re going to look at the generative adversarial networks behind AI-generated images, and help you to understand how to create and build your similar application with PyTorch. We’ll try to keep the post as intuitive as possible for those of you just starting out, but we’ll try not to dumb it down too much.
At the end of this article, you’ll have a solid understanding of how General Adversarial Networks (GANs) work, and how to build your own.
Task Overview
In this post, we will create unique anime characters using the Anime Face Dataset. It is a dataset…
