vae

Forward noising is fixed, reverse denoising is learned, and the training loss is a plain noise-prediction regression. The signal 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 underpin representation learning and generative modeling, including the VAE inside latent diffusion. The signal is the gap between a plain autoencoder and a variational one, and why you cannot backprop through sampling without the reparameterization trick.