vae

Forward noising is fixed, reverse denoising is learned, and the training loss is a plain noise-prediction regression. What interviewers reward is why that beats a GAN's minimax and what the VAE and U-Net each do in latent space. Here is the answer.

Autoencoders and VAEs sit beneath representation learning and generative modeling, including the VAE inside latent diffusion. What matters is the difference between a plain autoencoder and a variational one, and why sampling blocks backprop without the reparameterization trick.