Denoising Diffusion Probabilistic Models (DDPM)Product24/100 via “variational-lower-bound-training-objective”
* 🏆 2020: [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)](https://arxiv.org/abs/2010.11929)
Unique: DDPM derives the training objective from first principles using the variational lower bound, showing that the KL divergence terms simplify to an L2 loss on noise prediction when using a fixed linear noise schedule. This connection to score-matching provides both theoretical grounding and computational efficiency. The approach avoids the need for explicit likelihood computation or adversarial training, making it more stable than GANs.
vs others: More theoretically principled and stable than GAN training (no mode collapse, no discriminator equilibrium), more interpretable than VAE objectives (direct connection to likelihood), and enables fine-grained control over loss weighting across timesteps.