Basics of Variational Autoencoders (VAEs)
Learn how Variational Autoencoders enhance classic autoencoders by creating smooth, continuous latent spaces that allow for meaningful data generation. This lesson covers the VAE pipeline, the importance of probabilistic encoding, and how interpolation in latent space enables the creation of new, realistic data variations.
If you’ve ever tried to pack a suitcase for a long trip, you know the challenge: how do you squeeze everything you need into a tiny space, and still find what you want later? That’s the core idea behind autoencoders. They are neural networks designed to compress information into a smaller, more manageable form, then unpack it again as faithfully as possible.
Autoencoders matter because they teach machines to identify the essence of data, what’s truly important, and what can be discarded. This is crucial for tasks such as image compression, denoising, and, as we’ll see, generating new data that appears and feels realistic.
In this lesson, we’ll build upon the classic autoencoder, explore its limitations, and then see how Variational Autoencoders (VAEs) take things to the next level by using a key modification that enables them not only to reconstruct but also to generate new, meaningful data.
Autoencoder compression and reconstruction
An autoencoder is primarily a two-part system consisting of an encoder and a decoder. The encoder’s job is to take the input, say, a photo, and squeeze it down into a compact “code” or latent representation. The decoder then tries to unpack this code and reconstruct the original photo as closely as possible.
