Variational Autoencoders

Variational Autoencoders (VAEs) are a type of machine learning model used for generating new data that is similar to existing data. They work by compressing input data, like images, into a simpler form called a latent space and then reconstructing it back to the original format. VAEs introduce randomness in the compression process, allowing them to explore different variations of data. This helps in creating new, realistic images or patterns, finding applications in art, medicine, and more, by learning the underlying structure of the data rather than just memorizing it.