Contractive Diffusion Probabilistic Models

Understand

Diffusion probabilistic models (DPMs) have emerged as a promising technique in generative modeling.

• The success of DPMs relies on two ingredients: time reversal of diffusion processes and score matching.
• In view of possibly unguaranteed score matching, we propose a new criterion -- the contraction property of backward sampling in the design of DPMs, leading to a novel class of contractive DPMs (CDPMs).
• Our key insight is that, the contraction property can provably narrow score matching errors and discretization errors, thus our proposed CDPMs are robust to both sources of error.