Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning (1704.03976v2)

Abstract: We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only "virtually" adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10.

• The paper introduces a novel regularization approach using virtual adversarial training to improve the smoothness of model predictions.
• It applies the technique in both supervised and semi-supervised frameworks, demonstrating improved performance on benchmark tasks.
• Experimental results confirm that virtual adversarial perturbations reduce overfitting and enhance model robustness against adversarial examples.

Summary: Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals

The paper "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals," authored by Michael Shell, John Doe, and Jane Doe, presents an instructional guide for utilizing the IEEEtran.cls file, version 1.8b and later, in preparing manuscripts for IEEE Computer Society journals. This document serves as a comprehensive reference for researchers and practitioners in the field of Electrical and Computer Engineering and related disciplines who intend to publish their work in IEEE forums.

Intent and Scope

The primary objective of the paper is to facilitate the standardization and ease of manuscript preparation using LaTeX, specifically with the IEEEtran.cls class file. This approach is motivated by the need for consistency in formatting and layout, which is crucial for the IEEE publication process. The paper outlines the proper implementation of various components of a typical journal article, including title, abstract, section headings, appendices, acknowledgments, and references.

Main Contributions

The document meticulously details the following aspects:

1. Title and Abstract Formatting:
   • Provides guidelines for structuring the title, including author names and affiliations, crucial for visibility and indexing.
   • Specifies the formatting requirements for abstracts, ensuring they adhere to IEEE standards.
2. Section Hierarchy and Headings:
   • Explains the use of section, subsection, and subsubsection commands to maintain a logical flow and coherence throughout the document.
   • Demonstrates the importance of consistent heading styles to enhance readability and navigability.
3. Figures and Tables:
   • Illustrates the correct inclusion and formatting of figures and tables, emphasizing adherence to IEEE requirements for clarity and conciseness.
   • Addresses the captioning and referencing of visual elements within the text.
4. References and Citation:
   • Provides a template for the bibliography section, showcasing the proper citation format for various sources.
   • Highlights the importance of accurate and complete references in enhancing the credibility and verifiability of the research.
5. Appendices and Acknowledgments:
   • Discusses the use of appendices for supplementary material that supports but is not essential to the main text.
   • Offers a framework for acknowledgments, allowing authors to recognize contributors and funding agencies.

Practical and Theoretical Implications

The implications of this paper are primarily practical, aimed at authors preparing submissions for IEEE Computer Society journals. By adhering to the guidelines set forth in this document, researchers can significantly streamline the manuscript preparation and review process. This standardization not only benefits individual authors but also contributes to the efficiency and consistency of the IEEE publication ecosystem.

Theoretically, the paper underscores the importance of structured document preparation in scientific communication. It highlights how compliance with established formatting standards can enhance the dissemination and impact of research findings by ensuring documents are easily accessible and readable to the target audience.

Future Developments

Looking ahead, the ongoing updates and versions of the IEEEtran.cls file will likely continue to evolve, incorporating new features and accommodating changes in publication standards. Researchers and practitioners should stay informed about these updates to ensure their submissions remain compliant with IEEE requirements. Additionally, the integration of automated tools and templates into common LaTeX editors may further simplify the preparation process, reducing the manual effort required.

In conclusion, "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals" serves as a crucial resource for authors in the IEEE community. By providing a detailed guide to manuscript preparation, the paper enhances the quality and consistency of publications, ultimately contributing to the advancement of knowledge in the field of Electrical and Computer Engineering.