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Computer Science > Computer Vision and Pattern Recognition

arXiv:2204.00227 (cs)
[Submitted on 1 Apr 2022]

Title:Perception Prioritized Training of Diffusion Models

Authors:Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon
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Abstract:Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising score matching loss. In this paper, we show that restoring data corrupted with certain noise levels offers a proper pretext task for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function. We show that our simple redesign of the weighting scheme significantly improves the performance of diffusion models regardless of the datasets, architectures, and sampling strategies.
Comments: CVPR 2022 Code: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2204.00227 [cs.CV]
  (or arXiv:2204.00227v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2204.00227

Submission history

From: Jooyoung Choi [view email]
[v1] Fri, 1 Apr 2022 06:22:23 UTC (8,323 KB)
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