Variational Autoencoders (VAEs) vs Autoencoders: Complete Comparison

Autoencoders (AEs) and Variational Autoencoders (VAEs) are both neural network architectures used for data compression and representation learning. However, VAEs, unlike AEs, introduce a probabilistic approach and regularization, making their latent space more structured and suitable for generating new data, similar to how generative models like GANs and diffusion models operate.