Variational Autoencoder (VAE)
A Variational Autoencoder (VAE) is a generative model that combines principles from Bayesian inference and deep learning. It is designed to learn efficient representations of data, enabling the generation of new data that resembles the input data.
The architecture of a VAE consists of two main components: an encoder and a decoder. The encoder compresses input data into a lower-dimensional latent space, which captures the essential features of the data. Instead of mapping inputs to fixed points in this latent space, a VAE models the latent variables as distributions, typically Gaussian. This approach allows the model to incorporate uncertainty into its representations.
The decoder then samples from these distributions to reconstruct the original data. During training, the VAE optimizes two main objectives: minimizing the reconstruction loss, which measures how well the decoder can recreate the input data from the latent space, and maximizing the variational lower bound on the likelihood of the data. This involves a regularization term that encourages the learned latent distributions to resemble a standard normal distribution.
VAEs are particularly useful in tasks such as image generation, semi-supervised learning, and anomaly detection. They allow for the exploration of the latent space, enabling the generation of new, similar instances of the training data. As a result, VAEs have become popular in various applications, including computer vision, natural language processing, and more.