Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick
Towards Data Science
Read the full article
Variational Autoencoders (VAEs) improve upon vanilla autoencoders by addressing limitations in preserving similarity in latent space and enhancing image generation quality. VAEs enable more efficient data compression and better downstream task performance by learning probabilistic latent representations.
