Studying Classifier(-Free) Guidance From a Classifier-Centric
  Perspective
Classifier-free guidance has become a staple for conditional generation with
denoising diffusion models. However, a comprehensive understanding of
classifier-free guidance is still missing. In this work, we carry out an
empirical study to provide a fresh perspective on classifier-free guidance.
Concretely, instead of solely focusing on classifier-free guidance, we trace
back to the root, i.e., classifier guidance, pinpoint the key assumption for
the derivation, and conduct a systematic study to understand the role of the
classifier. We find that both classifier guidance and classifier-free guidance
achieve conditional generation by pushing the denoising diffusion trajectories
away from decision boundaries, i.e., areas where conditional information is
usually entangled and is hard to learn. Based on this classifier-centric
understanding, we propose a generic postprocessing step built upon
flow-matching to shrink the gap between the learned distribution for a
pre-trained denoising diffusion model and the real data distribution, majorly
around the decision boundaries. Experiments on various datasets verify the
effectiveness of the proposed approach.
67d47010fa4c0750013998b6
Xiaoming Zhao
claimed_verified
2025-03-16T21:13:30.987Z
642006ed290342c5df8158cc
/avatars/408d35ff2512eb93ed6570befa33aa03.svg
Xiaoming Zhao
user
xzhao
67d47010fa4c0750013998b7
Alexander G. Schwing
Studying Classifier(-Free) Guidance From a Classifier-Centric
  Perspective
Classifier-free guidance has become a staple for conditional generation with
denoising diffusion models. However, a comprehensive understanding of
classifier-free guidance is still missing. In this work, we carry out an
empirical study to provide a fresh perspective on classifier-free guidance.
Concretely, instead of solely focusing on classifier-free guidance, we trace
back to the root, i.e., classifier guidance, pinpoint the key assumption for
the derivation, and conduct a systematic study to understand the role of the
classifier. We find that both classifier guidance and classifier-free guidance
achieve conditional generation by pushing the denoising diffusion trajectories
away from decision boundaries, i.e., areas where conditional information is
usually entangled and is hard to learn. Based on this classifier-centric
understanding, we propose a generic postprocessing step built upon
flow-matching to shrink the gap between the learned distribution for a
pre-trained denoising diffusion model and the real data distribution, majorly
around the decision boundaries. Experiments on various datasets verify the
effectiveness of the proposed approach.
Classifier-free guidance and classifier guidance in denoising diffusion models achieve conditional generation by moving trajectories away from decision boundaries; a flow-matching-based postprocessing step improves the learned distribution around these boundaries.
classifier-free guidance classifier guidance denoising diffusion models decision boundaries flow-matching