Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick

A math-first walkthrough of Variational Autoencoders (VAEs), covering the limitations of vanilla autoencoders in latent space similarity and image generation, the VAE approximation approach using learned Gaussian distributions, the derivation of the Evidence Lower Bound (ELBO) as the training objective, the semantic meaning of its two terms (reconstruction loss equivalent to MSE and KL divergence regularization), and the reparameterization trick that enables backpropagation through the sampling operation.

Source: https://towardsdatascience.com/variational-autoencoders-vaes-explained-from-theory-to-elbo-and-the-reparameterization-trick. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.