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Perception Prioritized Training of Diffusion Models (2204.00227v1)

Published 1 Apr 2022 in cs.CV and cs.LG

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.

Overview of "LaTeX Guidelines for Author Response"

The document at hand provides a comprehensive set of guidelines intended for authors drafting a response to reviews received for their conference paper submission. This guidance is specifically tailored in the context of the CVPR conference series, emphasizing the importance of clarity and conciseness due to strict constraints on the rebuttal format.

Purpose and Restrictions

The primary aim of the author response is to engage with reviewers' comments, addressing factual inaccuracies or supplementing the originally submitted content with additional context requested by reviewers. However, it is critical to note that the rebuttal is not meant for introducing entirely new contributions or experiments not solicited in the review process. Furthermore, as endorsed in a 2018 PAMI-TC motion, reviewers are discouraged from requesting significant new experiments in their feedback, and any lack of additional experimental data should not be penalized in the review outcome.

Formatting Requirements

The document prescribes meticulous formatting standards that authors must adhere to in their rebuttal submissions, covering layout specifications such as font size, column dimensions, and margin size, thereby ensuring uniformity and ease of reading:

  • The text is required to be formatted in a two-column layout.
  • Responses are capped at a one-page limit, inclusive of references and figures.
  • Authors are urged to maintain the anonymity standard of their original paper submissions, precluding any external links that might disclose their identity.

Practical Implications and Best Practices

From a practical standpoint, the guidelines emphasize the effective use of illustrations and visual aids, such as figures and graphs, which can be incorporated to clarify or exemplify responses to reviewers’ comments. There is a strong advisement to ensure visual elements are of printable quality, with appropriately sized fonts and lines to match the document's primary text.

Moreover, while allowing graphical content in the rebuttal, the guidelines caution against overlapping internal references such as figures and tables, with those in the original submission, as this might lead to confusion during the review.

Implications for Future Research

The meticulous structuring of this rebuttal process reflects the broader academic emphasis on transparency and rigor in scholarly dialogues. By setting explicit constraints and expectations, these guidelines aim to enhance the efficiency and effectiveness of the peer review process. For AI researchers, particularly those focused on computational vision and pattern recognition, adhering to such structured methodologies not only facilitates constructive critique but also sharpens the clarity and impact of their scholarly communications.

Looking forward, these guidelines underscore the potential for developing more standardized and streamlined peer review protocols through AI-based systems, which could further aid authors in crafting compliant submissions effectively. Furthermore, as conference formats evolve, analogous adaptive strategies might be considered to optimize rebuttal processes in different scientific fields.

In conclusion, "LaTeX Guidelines for Author Response" serves as an essential framework for authors seeking to engage in the scholarly review process within the CVPR community, underscoring the need for precision and adherence to formal protocol in academic discourse.

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Authors (6)
  1. Jooyoung Choi (21 papers)
  2. Jungbeom Lee (16 papers)
  3. Chaehun Shin (12 papers)
  4. Sungwon Kim (32 papers)
  5. Hyunwoo Kim (52 papers)
  6. Sungroh Yoon (163 papers)
Citations (195)