Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems

The book provides a timely coverage of the paradigm of knowledge distillation-an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher-student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.

Inhaltsverzeichnis

Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation.- A Geometric Perspective on Feature-Based Distillation.- Knowledge Distillation Across Vision and Language.- Knowledge Distillation in Granular Fuzzy Models by Solving Fuzzy Relation Equations.- Ensemble Knowledge Distillation for Edge Intelligence in Medical Applications.- Self-Distillation with the New Paradigm in Multi-Task Learning.- Knowledge Distillation for Autonomous Intelligent Unmanned System.

Produktdetails

• Mitarbeit: Chen (Herausgeber), Shyi-Ming Chen (Herausgeber), Witold Pedrycz (Herausgeber)
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Taschenbuch
• Erschienen: 15.06.2024
• EAN: 9783031320972
• ISBN: 978-3-0-3132097-2
• Seiten: 232
• Abmessung: 155 mm x 13 mm x 235 mm
• Gewicht: 377 g
• Illustration: VIII, 232 p. 70 illus., 51 illus. in color.
• Serie: Studies in Computational Intelligence
• Thema: Naturwissenschaften, Medizin, Informatik, Technik > Technik > Allgemeines, Lexika