Variational Autoencoders (VAEs) | Vibepedia

Variational Autoencoders (VAEs) are a type of deep learning model that combines the capabilities of autoencoders and generative models, allowing for efficient dimensionality reduction, feature learning, and data generation. Developed by researchers such as David Kingma and Max Welling, VAEs have been widely used in applications like image and speech recognition, natural language processing, and recommender systems. Companies like Google, Facebook, and Microsoft have also explored the potential of VAEs in their products and services.