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Advancements in knowledge distillation: towards new horizons of intelligent systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advancements in knowledge distillation: towards new horizons of intelligent systemsedited by Witold Pedrycz, Shyi-Ming Chen.
其他作者:	Pedrycz, Witold.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	viii, 232 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence.
電子資源:	https://doi.org/10.1007/978-3-031-32095-8
ISBN:	9783031320958$q(electronic bk.)

Advancements in knowledge distillation: towards new horizons of intelligent systems
Advancements in knowledge distillation: towards new horizons of intelligent systems[electronic resource] /edited by Witold Pedrycz, Shyi-Ming Chen. - Cham :Springer International Publishing :2023. - viii, 232 p. :ill. (chiefly col.), digital ;24 cm. - Studies in computational intelligence,v. 11001860-9503 ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783031320958$q(electronic bk.)

Standard No.: 10.1007/978-3-031-32095-8doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Advancements in knowledge distillation: towards new horizons of intelligent systems
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