First of all, check out this cool video by Jun-Yan Zhu:

To me the amazing thing about this is not the fact that we are turning a horse into a zebra, but how it's done.
There is absolutely no frame-by-frame video editing or any trickery of that sort involved. Just a six neural network configuration that's learned to do this on it's own based on the data that's been used to train it.

What's a Generative Adversarial Network?

One of the hottest kinds of neural network models at the time of writing this post is what is known as the Generative Adversarial Network (GAN for short). It was invented by a guy named Ian Goodfellow and like a lot of the awesome ideas in this world, it all started with an argument at a bar. It's a configuration of several neural networks that fall into two main categories:

1) Generative networks

These are the networks that generate new things based on the input that they've been trained. A great analogy I'm stealing from Siraj Raval's video (You should really watch it if you want to learn more!) is that a generative network is like a magician trying to convince an audience with his magic trick. Or in other words, it tries to generate a believable zebra in our case.

2) Discriminative networks

The discriminative networks in GAN are the audience to the magician and their job is to judge how well the generative networks do their job, or if their magic trick was believable or not.

So what's actually going on in a GAN is what's known as a game theoretical zero-sum game.

Here's a great 5-minute video from Siraj Raval that explains the concept far better than I ever could:

Links & further reading:

1. The paper explaining the concept behind the "horse to zebra demo" by Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
2. The original paper introducing GANs by Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
3. Two-hour talk by Ian Goodfellow on GANs