University of Koblenz and Landau

The work proposes a Hierarchical Diffusion Classifier (HDC) that exploits parent-child label hierarchies: in a pruning stage it traverses the label tree level by level, estimates epsilon-prediction errors with fewer Monte Carlo samples, and keeps only the lowest-error nodes, then runs classical diffusion classification on the surviving leaf nodes, achieving roughly 60% inference speed-up on ImageNet-1K with Stable Diffusion 2.0 (1600s to 650s) or raising per-class accuracy from 64.90% to 65.16% at nearly unchanged runtime.

The work proposes a Hierarchical Diffusion Classifier (HDC) that exploits parent-child label hierarchies: in a pruning stage it traverses the label tree level by level, estimates epsilon-prediction errors with fewer Monte Carlo samples, and keeps only the lowest-error nodes, then runs classical diffusion classification on the surviving leaf nodes, achieving roughly 60% inference speed-up on ImageNet-1K with Stable Diffusion 2.0 (1600s to 650s) or raising per-class accuracy from 64.90% to 65.16% at nearly unchanged runtime.