What are Variational Autoencoders (VAEs)? [2023]

Variational Autoencoders (VAEs) are generative models that combine the concepts of autoencoders and variational inference to learn and generate new data samples. VAEs are a type of unsupervised learning algorithm that excel at capturing the underlying distribution of complex, high-dimensional data.

An autoencoder is a neural network architecture consisting of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional latent space representation, while the decoder reconstructs the original input from the latent space representation. Autoencoders are primarily used for dimensionality reduction and data compression.

In contrast, VAEs introduce a probabilistic component to the latent space by enforcing a distribution on the encoder's outputs. Instead of directly encoding the input into a fixed point in the latent space, VAEs learn to model the underlying distribution of the data. This probabilistic approach enables VAEs to generate new samples by sampling from the learned latent space.

The key innovation of VAEs lies in their ability to learn the parameters of the latent space distribution using variational inference. By assuming an initial simple prior distribution (often a Gaussian distribution) in the latent space, VAEs aim to match this prior distribution with the posterior distribution of the encoded latent variables conditioned on the input data.

During training, VAEs optimize two objectives: reconstruction loss and the Kullback-Leibler (KL) divergence between the learned distribution and the prior distribution. The reconstruction loss ensures that the decoder can accurately reconstruct the original input, while the KL divergence encourages the learned distribution to match the prior distribution.

Once trained, VAEs allow for the generation of new samples by sampling latent variables from the prior distribution and passing them through the decoder network. This sampling process allows VAEs to generate diverse and novel samples that resemble the training data.

VAEs have been applied to various domains, including image generation, text generation, and data synthesis. They provide a powerful framework for unsupervised learning and have contributed to advances in generative modeling and representation learning.