A Comprehensive Analysis of Deep Regression (1803.08450v3)

Abstract: Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e. convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.

Overview of the "Bare Demo of IEEEtran.cls for IEEE Computer Society Conferences"

The paper, "Bare Demo of IEEEtran.cls for IEEE Computer Society Conferences" by Michael Shell, Homer Simpson, James Kirk, and Montgomery Scott, serves primarily as a structural blueprint for authors preparing manuscripts in LaTeX for IEEE Computer Society conferences. It provides a template using IEEEtran.cls, which is a LaTeX class file extensively utilized for drafting papers suitable for IEEE formats, specifically targeting version 1.8b or later.

Structural and Technical Composition

The document outlines the essential framework and components expected from authors when submitting papers for IEEE Computer Society conferences. The template includes crucial sections such as the title, abstract, author information, introduction, multiple sections with subsections, and a conclusion. Additionally, it prescribes the Acknowledgment section, formatted differently based on the class options, and references formatted using IEEE style.

Key technical features highlighted include:

• Proper setup for the author's institutional information and contact details.
• Formatting guidelines for sections and subsections, ensuring clarity and consistency throughout the document.
• Instructions for embedding figures, tables, mathematical equations, and bibliographies, which are pivotal for technical manuscripts.

Implications and Relevance

This template is instrumental for standardizing submissions, thereby ensuring uniformity and enabling easier processing, review, and dissemination of conference papers. Through these structured guidelines, authors can focus more on content quality rather than format specifications, which could enhance overall scientific communication efficiency.

The practical implications extend to improving the accessibility of technical papers for both readers and reviewers. By conforming to a universally recognized format, the exchange of academic and technical knowledge is streamlined across diverse disciplines within the IEEE community. This consistency is vital as it facilitates better indexing, citation, and retrieval of scholarly works.

Future Developments

While this paper outlines the use of IEEEtran.cls for IEEE Computer Society conferences, one might consider speculative directions for future improvements or iterations. As LaTeX evolves and integrates more sophisticated typesetting features, future templates could incorporate automated tools for enhanced document validation, metadata generation, and integration with contemporary digital publication platforms. These advancements could further reduce preparation time, minimize formatting errors, and foster new modes of interactive content presentation, aligning with evolving trends in digital publishing.

In conclusion, Michael Shell et al.'s template serves as an essential resource for guiding authors in preparing IEEE-compliant submissions, ensuring consistency, and contributing toward efficient academic communication. As the scientific publishing landscape continues to transform, tools like IEEEtran.cls are likely to adapt, aligning with new technologies and authorship practices, reinforcing the primacy of effective document standardization.