Continual Semi-Supervised Learning: First International Workshop, CSSL 2021, Virtual Event, August 19-20, 2021, Revised Selected Papers

This book presents the proceedings of the First International Workshop on Continual Semi-Supervised Learning,CSSL 2021,which includes 9 full papers and 0 short papers selected from 14 submissions.

Format: Paperback / softback
Length: 135 pages
Publication date: 28 September 2022
Publisher: Springer International Publishing AG

The book serves as a valuable resource for researchers, practitioners, and students interested in the field of Continual Semi-Supervised Learning, providing a comprehensive overview of the latest advancements and trends in this rapidly evolving area.
The book is a compilation of the proceedings of the First International Workshop on Continual Semi-Supervised Learning (CSSL 2021), which took place virtually in August 2021. It features a selection of 9 full papers and 0 short papers, meticulously reviewed and chosen from a total of 14 submissions. This publication serves as an invaluable resource for researchers, practitioners, and students actively engaged in the domain of Continual Semi-Supervised Learning. It offers a comprehensive exploration of the latest developments and emerging trends in this rapidly evolving field, presenting cutting-edge research and insights.

The book begins with an introductory chapter that provides a comprehensive overview of Continual Semi-Supervised Learning, its applications, and the challenges it presents. This chapter serves as a foundation for the subsequent chapters, which delve into specific topics and research areas.

One of the key themes explored in the book is the use of unlabeled data for Continual Semi-Supervised Learning. The authors discuss various techniques and algorithms that leverage unlabeled data to improve the performance of supervised learning models. These techniques include self-training, transfer learning, and semi-supervised learning.

Another important aspect of Continual Semi-Supervised Learning is the integration of domain knowledge. The authors explore how domain knowledge can be incorporated into the learning process to enhance the accuracy and generalization of semi-supervised learning models. They discuss various approaches, such as knowledge distillation, feature extraction, and knowledge-based optimization.

The book also includes case studies and practical applications of Continual Semi-Supervised Learning in various domains, such as image classification, speech recognition, and natural language processing. The authors showcase the effectiveness of the proposed techniques and algorithms in real-world scenarios and provide insights into their limitations and future directions.

In conclusion, the book serves as a comprehensive and up-to-date reference for researchers, practitioners, and students interested in Continual Semi-Supervised Learning. It provides a deep understanding of the latest advancements and trends in this field, covering a wide range of topics and applications. With its valuable insights and practical examples, this publication will be an invaluable resource for anyone seeking to explore and advance the field of Continual Semi-Supervised Learning.

Weight: 250g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031175862
Edition number: 1st ed. 2022