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Record Nr. |
UNINA9910739443203321 |
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Autore |
Pedrycz Witold |
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Titolo |
Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems / / edited by Witold Pedrycz, Shyi-Ming Chen |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (239 pages) |
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Collana |
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Studies in Computational Intelligence, , 1860-9503 ; ; 1100 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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advanced learning paradigms. |
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