Call for Chapters: Exploring Generative Adversarial Networks and Meta-Learning Synergies

Editors

Sarita Lilhore, Galgotias University, India
Sarita Simaiya, University of Louisiana, India
Yogesh Sharma, KL University, India
Sandeep Kumar, KL University, India

Call for Chapters

Proposals Submission Deadline: July 18, 2024
Full Chapters Due: October 31, 2024
Submission Date: October 31, 2024

Introduction

"Exploring Generative Adversarial Networks and Meta-Learning Synergies" delves into the intricate interplay between two cutting-edge fields in machine learning: generative adversarial networks (GANs) and meta-learning. Authored by experts in the field, this book provides a comprehensive overview of both GANs and meta-learning techniques, exploring their individual capabilities and the synergies that emerge when they are combined. Through clear explanations, illustrative examples, and practical applications, this book serves as a valuable resource for researchers, practitioners, and students seeking to understand and leverage the potential of these powerful machine learning paradigms.

Objective

The objective of "Exploring Generative Adversarial Networks and Meta-Learning Synergies" is to provide readers with a deep understanding of both generative adversarial networks (GANs) and meta-learning, and to explore the ways in which these two fields can complement and enhance each other. The book aims to: -Present a comprehensive overview of GANs and meta-learning techniques, including their principles, algorithms, and applications. -Investigate the potential synergies between GANs and meta-learning, highlighting how combining these approaches can lead to novel solutions and improved performance in various machine learning tasks. -Offer clear explanations, illustrative examples, and practical insights to help readers grasp the concepts and apply them in their own research or projects. -Serve as a valuable resource for researchers, practitioners, and students interested in exploring the forefront of machine learning and leveraging advanced techniques for generative modeling, learning from limited data, and adapting to new tasks and environments.

Target Audience

The target audience for "Exploring Generative Adversarial Networks and Meta-Learning Synergies" includes: -Researchers: Professionals and academics in the fields of machine learning, artificial intelligence, computer science, and related disciplines who are interested in gaining a deeper understanding of GANs and meta-learning, as well as exploring their synergies. -Practitioners: Engineers, data scientists, and machine learning practitioners who want to learn about state-of-the-art techniques in generative modeling, data-efficient learning, and adaptive learning systems for real-world applications. -Students: Graduate students and advanced undergraduate students studying machine learning, deep learning, or related subjects who seek to expand their knowledge and expertise in advanced topics such as GANs and meta-learning. -Industry Professionals: Professionals working in industries such as computer vision, natural language processing, healthcare, finance, and entertainment who wish to stay updated on the latest developments in machine learning and explore how GANs and meta-learning can be applied to solve practical problems and drive innovation.

Recommended Topics

1. Introduction to Generative Adversarial Networks (GANs) and Meta-Learning 2. Theoretical Foundations of GANs and Meta-Learning 3. Applications of GANs in Image Generation and Style Transfer 4. Applications of Meta-Learning in Few-Shot Learning and Domain Adaptation 5. Challenges and Limitations of GANs and Meta-Learning 6. Synergies between GANs and Meta-Learning: Theoretical Perspectives 7. Synergies between GANs and Meta-Learning: Practical Implementations 8. Case Studies and Experiments Demonstrating the Synergies 9. Future Directions and Emerging Trends in GANs and Meta-Learning 10. Ethical Considerations and Implications of GANs and Meta-Learning Applications 11. Advanced Techniques in GANs: Wasserstein GANs, Self-Attention GANs 12. Meta-Learning Architectures: MAML, Reptile, and Their Variants 13. Transfer Learning with GANs and Meta-Learning 14. Generative Models in Healthcare: Applications and Challenges 15. GANs and Meta-Learning in Robotics: Enhancing Autonomy and Adaptability

Submission Procedure

Researchers and practitioners are invited to submit on or before July 18, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 1, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by October 31, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Exploring Generative Adversarial Networks and Meta-Learning Synergies. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

July 18, 2024: Proposal Submission Deadline
August 1, 2024: Notification of Acceptance
October 31, 2024: Full Chapter Submission
January 2, 2025: Review Results Returned
February 13, 2025: Final Acceptance Notification
February 27, 2025: Final Chapter Submission



Inquiries

Umesh Kumar Lilhore
University of Louisiana,
Galgotias University
umeshlilhore@gmail.com

Sarita Simaiya
Galgotias University
saritasimaiya@gmail.com

Yogesh Sharma
KL University
dr.sharmayogeshkumar@gmail.com

Sandeep Kumar
KL University
er.sandeepsahratia@gmail.com



Classifications


Computer Science and Information Technology; Medicine and Healthcare
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