TY  - JOUR
TI  - Denoising Diffusion Probabilistic Models (DDPM)
AU  - Ho, J.
AU  - Jain, A.
AU  - Abbeel, P.
PY  - 2020
DA  - 2020/06//
JO  - NeurIPS 2020
AB  - Ho, Jain and Abbeel (2020) demonstrated that a neural network trained to reverse a fixed Markov noising chain can generate high-quality images by iteratively denoising Gaussian noise. The denoising score-matching objective gives DDPM a stable training regime compared to GANs, and the resulting sample quality matched or exceeded the state of the art. DDPM is the pixel-space origin of the entire diffusion stack: Stable Diffusion, AnimateDiff, Video LDM and the Motion Diffusion Model all build on its mathematical foundation. It is the paper courses name when introducing diffusion models to creative-AI students.
KW  - generative-ai
KW  - image-generation
UR  - https://arxiv.org/abs/2006.11239
LA  - en
ER  -