Mastering Variational Autoencoders (VAEs): Unlocking the Power of Generative AI

 Variational Autoencoders are the generative models that can create music and generate fake human faces. They were developed by Diederik P. Kingma and Max Welling who combined autoencoders with variational Bayesian methods.

In this blog, we’ll explore how Variational autoencoders work by comparing them to a musician composing new melodies. Let’s tune in!

Playlist: Dataset

You have a playlist of your favorite musicians, this playlist includes several songs with their unique rhythms, tone, melody and harmony. In our analogy, this playlist acts as a dataset that will be fed to variational autoencoders. Just as how a musician can understand a song VAEs can also understand the training dataset by capturing underlying patterns.

Understanding Music: Encoder

To create any new musical piece a musician can take inspiration from various sources, our encoder does this as well. To make a new song encoder breaks down existing songs into pieces capturing their distribution and leaving out unimportant details. This toned-down version of a song is called the latent variable or latent representation.

The Music Sheet: A Latent Space

Now once a musician gets inspired by some features of their best hit songs they then try to map all the possible melodies and harmonies on their music sheet. This map is our latent space. Each point on this map represents a different combination of musical elements, with similar songs placed close together.

This space allows the VAE to explore a wide range of possibilities, including new combinations that might not have been part of the original dataset.

The Composer’s Creativity: The Decoder

Once the musician has analyzed the songs and created a music sheet (latent space), it’s time to compose new music. The decoder in a VAE is like the musician’s creative mind, taking a point from the latent space (a combination of melodies and rhythms) and using it to generate a new song.

The decoder mixes and matches these abstract musical elements back into a full composition — a new data point that resembles the original data.

Basic Architecture of Variational autoencoder

Variations on a Theme: The “Variational” Part

But we as a listener don't want to listen to what we already have. To make a hit musician must do something different this time, we want variation!

In VAE terms, the encoder doesn’t just replicate a single point in the latent space for each song. Instead, it generates a range of possibilities, represented by a mean and standard deviation. This introduces some randomness, allowing the decoder to explore the latent space more thoroughly and generate a broader array of new compositions.

Creating New Hits: Data Generation

Because our musician’s map (the latent space) is continuous, they can use it to compose entirely new songs that have never been heard before. By sampling a point in this space and feeding it into the decoder, the musician can create new melodies that sound like they belong on the original playlist but bring something fresh to the table.

In practical terms, this means that a VAE can generate new data points — new images, new text, or new music — that are similar to what it’s been trained on but with their unique twists.

Conclusion: A Symphony of Creativity

Understanding Variational Autoencoders becomes easier when we think of them as musicians who analyze, destroy and then reconstruct an entirely new melody. The encoder captures the essence of the data, the latent space provides a canvas for creativity, and the decoder brings new data to life. By introducing variation into the process, VAEs can explore a wide range of possibilities, making them powerful tools for generating new and exciting data.

Next time you hear about VAEs, think of them as musicians who can not only play existing tunes but also compose entirely new symphonies from the underlying patterns they’ve learned.

To learn more about Variational autoencoders and about the math behind them check out this article.

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