Variational autoencoders, or VAEs, are a type of generative neural network used to learn complicated probability distributions of data. They are a combination of two components—an encoder and a decoder network. The encoder network is used to map a data input—such as an image—to a point in a probability distribution—the “latent space”—where predictions are made. The decoder network is then used to take the point in the latent space and generate an output—such as a reconstructed image—based on the probability distribution.
The main purpose of a variational autoencoder is to enable “latent space sampling,” or the ability to generate predictions from a given set of point(s) in the latent space. For example, a variational autoencoder could be used to generate a collection of images from diverse points in the latent space, such as faces of different genders and ethnicities.
Variational autoencoders are also used as generative models, or models that generate new data points from the given probability distribution. This could be applied in computer vision applications, such as image-driven data augmentation, or image generation.
One of the main advantages of using variational autoencoders is that they are used to learn complex probability distributions over the data and are data-efficient, meaning they can learn from a relatively small dataset. This makes them ideal for solving problems such as anomaly detection, where they can be used to identify out-of-distribution data points in an image or text.
Variational autoencoders also have potential applications in many other areas, such as natural language processing, as they can map words to various points in the latent space to generate novel phrases or sentences.
Overall, variational autoencoders offer a powerful solution to many types of generative modeling tasks, and are an invaluable tool in the fields of data science and machine learning.
