A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning (2307.09218v3)

Abstract: Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new task, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications. A comprehensive list of papers about forgetting in various research fields is available at \url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}.

• The paper presents a comprehensive guide to using IEEEtran.cls for formatting IEEE Computer Society journal manuscripts.
• It details key formatting components like title, author information, sections, abstracts, and references to ensure compliance with IEEE standards.
• The tutorial also discusses potential future enhancements in LaTeX typesetting to further simplify the manuscript preparation workflow.

An Overview of the IEEEtran.cls Usage in IEEE Computer Society Journals

The document titled "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals" is a tutorial-style paper created to assist authors in using the IEEEtran.cls class for \LaTeX, which is integral for formatting papers pertaining to IEEE Computer Society journals. The authors, Michael Shell, John Doe, and Jane Doe, aim to provide a foundational template that facilitates the typesetting process for researchers preparing manuscripts for submission to IEEE journals.

Objective and Structure

The primary objective of this document is to elucidate the usage of the IEEEtran.cls version 1.8b and later, particularly in the context of \LaTeX\ typesetting. This template serves as an exemplary starting point by outlining the structural components customary to IEEE Computer Society journals. It systematically introduces sections such as the Introduction, Subsections, and the Conclusion, along with Appendices and Acknowledgments, giving authors a comprehensive understanding of the document's flow.

Key Elements

• Title and Author Information Formatting: The paper underscores the correct methods for inserting author affiliations and contact information. This can significantly affect the manuscript’s acceptance if not properly aligned with IEEE standards.
• Abstract and Keywords: While the document does not include a real abstract, it distinguishes the importance of these sections in encapsulating the essence and relevance of the research.
• Sectioning Commands: By demonstrating the use of \LaTeX\ sectioning commands, the template guides users in organizing their paper effectively, ensuring logical flow and adherence to IEEE formatting rules.
• Appendices and References: The inclusion of appendices is discussed, as well as the formatting of references, highlighting critical compliance areas for maintaining academic rigor in research dissemination.

Implications and Speculation on Future Developments

Though this paper is fundamentally a template, its impact is notable for ensuring consistency in IEEE publications. Consistent formatting is crucial for readability and for maintaining a professional standard across publications, which ultimately supports the dissemination of high-quality research findings.

As \LaTeX\ evolves, it can be speculated that future iterations of IEEEtran.cls will continue to improve in usability and accessibility. Potential developments may focus on automating updates to comply with revised publication standards or enhancing compatibility with collaborative writing and version control tools. Such advancements would align with the growing demand for more efficient and integrated academic publishing workflows.

In conclusion, the "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals" is an indispensable tool for researchers aiming to publish within the IEEE framework. Its emphasis on providing a structured format supports both novice and experienced researchers in aligning their submissions with the professional standards expected in IEEE publications. Future enhancements in document preparation tools will likely continue to streamline and optimize the journey from manuscript preparation to publication.