- ホーム
- > 電子洋書
Description
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.
Table of Contents
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.
-
- 電子書籍
- ビデオサロン 2025年9月号
-
- 電子書籍
- LDK 2022年4月号 LDK
