title: Studying Classifier(-Free) Guidance From a Classifier-Centric
    Perspective
summary: 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.
paper_authors._id: 67d47010fa4c0750013998b6
paper_authors.name: Xiaoming Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:13:30.987Z
paper_authors._id: 642006ed290342c5df8158cc
paper_authors.avatarUrl: /avatars/408d35ff2512eb93ed6570befa33aa03.svg
paper_authors.fullname: Xiaoming Zhao
paper_authors.type: user
paper_authors.user: xzhao
paper_authors._id: 67d47010fa4c0750013998b7
paper_authors.name: Alexander G. Schwing
paper_title: Studying Classifier(-Free) Guidance From a Classifier-Centric
    Perspective
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: classifier-free guidance classifier guidance denoising diffusion models decision boundaries flow-matching