A Visual Guide To Variational Autoencoders (VAEs)

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A Visual Guide To Variational Autoencoders (VAEs)

AI, But Simple Issue #45

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Variational autoencoders (VAEs) are autoencoder-based neural networks used for generative tasks such as image generation or reconstructing missing data.

They are comparable to traditional autoencoders but use probabilistic techniques to not only reconstruct input data but also generate new, similar data to what they were trained on.

The most popular use cases for VAEs are:

• Generating new images, such as faces or digits.

• Anomaly detection by identifying data with high reconstruction error.

• Dimensionality reduction, reducing data dimensions for visualization.

Being a generative model, its performance is compared to other similar generative models, such as GANs or diffusion models. Want to learn more about GANs? We did a deep dive in one of our previous issues:

• Generative Adversarial Networks, Simply Explained (Intermediate)

At its heart, a VAE still has the same structure and components as a standard autoencoder: it has both an encoder and a decoder.

Like standard autoencoders, VAEs map inputs into a latent space (a smaller, lower-dimensional representation), then reconstruct latent space vectors back to the input space.

The key feature of VAEs is that, unlike regular autoencoders (which map inputs to fixed points in the latent space), VAEs learn a probability distribution over the data.

Given an input, the encoder maps it to a distribution in the latent space, outputting the parameters (mean and variance) of the distribution.

The decoder samples from this distribution back to the input space, reconstructing the data and generating new samples. Sampling from the learned distribution allows the model to obtain new but similar data points.

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Why Not Use A Standard Autoencoder?

Standard autoencoders don’t have good generative abilities since the latent space is deterministic. This means for a given input, the encoder produces a fixed output. Every time you pass the same input, the same latent vector is produced.

When we sample from a regular autoencoder, since the encoder produces a single latent vector for each input, we would get the same result every time—there's no way to sample new latent points that correspond to new data.