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.
General
Imprint: |
Springer International Publishing AG
|
Country of origin: |
Switzerland |
Series: |
Studies in Computational Intelligence, 1100 |
Release date: |
June 2023 |
First published: |
2023 |
Editors: |
Witold Pedrycz
• Shyi-Ming Chen
|
Dimensions: |
235 x 155mm (L x W) |
Pages: |
232 |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-132094-1 |
Categories: |
Books
|
LSN: |
3-03-132094-8 |
Barcode: |
9783031320941 |
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