On Feature Decorrelation in Self-Supervised Learning (2105.00470v2)

Abstract: In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentations. A potential issue of this idea is the existence of completely collapsed solutions (i.e., constant features), which are typically avoided implicitly by carefully chosen implementation details. In this work, we study a relatively concise framework containing the most common components from recent approaches. We verify the existence of complete collapse and discover another reachable collapse pattern that is usually overlooked, namely dimensional collapse. We connect dimensional collapse with strong correlations between axes and consider such connection as a strong motivation for feature decorrelation (i.e., standardizing the covariance matrix). The gains from feature decorrelation are verified empirically to highlight the importance and the potential of this insight.

• The paper introduces a novel decorrelation loss function that effectively reduces feature redundancy in self-supervised models.
• It demonstrates that incorporating decorrelation enhances representation diversity and boosts benchmark performance.
• The methodology integrates decorrelation into training seamlessly, offering actionable insights for robust self-supervised learning.

Author Guidelines for ICCV Proceedings

The document titled "LaTeX Author Guidelines for ICCV Proceedings" provides comprehensive instructions for authors intending to submit papers to the International Conference on Computer Vision (ICCV). It meticulously outlines the requirements for formatting, submission, and presentation of the manuscript, ensuring uniformity and adherence to the conference's standards. Given the technical expertise expected from these submissions, the guidelines are precise and deeply rooted in academic convention, catering to an audience familiar with LaTeX and academic publishing norms.

Content Overview

• Abstract and Introduction: The paper mandates that the abstract be presented in a standardized format, emphasizing brevity and clarity. Following the abstract, the introduction provides a step-by-step guide on using the IEEE Computer Society Press format and highlights changes in submission guidelines that authors must adhere to. Notably, sticky tape warnings are no longer part of these guidelines, indicating an evolution in document submission practices.
• Dual Submission and Blind Review: Authors are urged to consult the ICCV web page for policies regarding dual submissions and blind review processes. Particular attention is paid to maintaining anonymity while allowing citations of previous work by the authors themselves.
• Paper Length and Formatting: A strict limit of eight pages, excluding the references section, is enforced, with precise instructions on formatting text, figures, tables, and mathematical equations. Submissions exceeding this limit will not be reviewed, which stresses the importance of concise and compact presentation of technical content.
• Mathematics and Use of Language: The guidelines underscore the necessity of numbering all equations and sections, facilitating easy reference by future readers. Additionally, manuscripts must be written in English, ensuring broad accessibility within the academic community.
• Rulers and Review Process: The inclusion of a ruler in the review version of the paper is highlighted as an aid for reviewers to comment efficiently on specific lines, enhancing the critique process.
• Formatting Specifics: Detailed instructions on font style and size, page layout, title capitalization, and section headings are provided to maintain consistency across publications. The document advocates for fully justified text and cautions against the use of footnotes unless absolutely necessary.

Implications

These guidelines serve as a crucial tool for researchers aiming to publish in the ICCV proceedings. They reflect the conference's emphasis on quality presentation and the structured communication of scientific ideas, which are critical in advancing the field of computer vision. Adherence to these instructions ensures that submissions meet the professional and academic aesthetic valued by ICCV.

Future Speculation

Looking forward, it is conceivable that the guidelines will continue evolving to incorporate advances in typesetting technology and accommodate new modes of academic communication. The core principles of clarity, brevity, and uniformity are likely to remain central, but additional features leveraging digital formats and collaborative platforms might be anticipated. These changes will need to balance the traditional rigors of academic publishing with efficient, modern dissemination techniques.

Overall, the ICCV guidelines contribute significantly to the structured presentation of groundbreaking research and maintain the high standards necessary for impactful contributions to computer vision and AI.