Variational Autoencoders | Vibepedia

Variational Autoencoders (VAEs) are a class of deep generative models. They learn a compressed, probabilistic latent representation of data, allowing for the generation of new, similar data points. Unlike traditional autoencoders that map inputs to fixed latent vectors, VAEs map inputs to probability distributions (typically Gaussian) in the latent space. This probabilistic approach enables smooth interpolation between data points and the generation of novel samples by sampling from the learned latent distribution. VAEs have found widespread applications in image generation, anomaly detection, and representation learning, forming a cornerstone of modern generative AI research.