Variational Autoencoders (VAEs)

Variational Autoencoders (VAEs) are a type of artificial intelligence model that learns to create new data similar to existing data. They work by compressing data into a smaller representation (encoding) and then reconstructing it (decoding). Unlike traditional autoencoders, VAEs add a probabilistic layer, allowing them to generate diverse outputs by sampling from a learned distribution. This capability makes VAEs useful in generating realistic images, enhancing data, and developing new content by blending aspects of existing data, thus offering powerful tools for creativity in fields like art, music, and design.