Today we’re going to talk about something that’ll blow your mind: Variational Autoencoders (VAEs) for image compression. Yes, you heard me right this is not a drill!

Before anything else, let’s start with the basics. VAEs are a type of neural network that can learn to compress data by encoding it into a lower-dimensional latent space (a fancy way of saying “hidden variables”). This means we can represent our images using fewer bits without losing too much information!

Now, you might be wondering: why would anyone want to do this? Well, for starters, compressing data is essential in the world of AI. It allows us to store and transmit large amounts of data more efficiently, which can save time and money (and who doesn’t love saving some cash?!).

But that’s not all! VAEs also have other benefits, such as being able to generate new images from scratch or reconstructing missing parts of an image. This is because the latent space can be thought of as a sort of “image generator” we just need to provide it with some input and let it do its thing!

So, how does this work in practice? Let’s take a look at the VAE architecture:

As you can see, there are two main components to a VAE: an encoder and a decoder. The encoder takes in our input image (let’s say it’s 1024×1024 pixels), and compresses it into a lower-dimensional latent space using some fancy math. This is where the magic happens!

The decoder, on the other hand, takes this compressed representation and tries to reconstruct the original image as closely as possible. The goal here is to minimize the difference between the input image and its reconstruction (which we call “reconstruction loss”).

But wait there’s more! In order to make sure our VAE doesn’t overfit or underfit, we need to add a regularization term called KL divergence. This helps us ensure that the latent space is not too complex (which can lead to overfitting) and also encourages the encoder to learn meaningful features instead of just memorizing individual pixels.

So, how do we train our VAE? Well, first we need to define a loss function that combines both reconstruction loss and KL divergence:

As you can see, the loss is made up of two parts: reconstruction loss (which we want to minimize) and KL divergence (which we also want to minimize). The tricky part here is finding a balance between these two terms if one is too large or small compared to the other, our VAE won’t perform as well.

Now that we have our loss function defined, how we can use it for image compression! The idea is simple: instead of storing the original input image (which would take up a lot of space), we store its compressed representation in the latent space. This way, when someone wants to view or manipulate the image, they can simply decode it using our VAE’s decoder.

But wait there’s more! We can also use this technique for lossy compression (which means we sacrifice some quality in order to save even more space). By increasing the dimensionality of the latent space, we can represent images with fewer bits while still maintaining a reasonable level of quality.

This is just scratching the surface of what’s possible with this technique, but hopefully, I’ve given you enough information to get started on your own projects.