東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Knowledge distillation in computer v...

Zhang, Linfeng.

FindBook

Google Book

Amazon

博客來

Knowledge distillation in computer vision

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Knowledge distillation in computer vision/ by Linfeng Zhang.
作者:	Zhang, Linfeng.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	viii, 140 p. :ill., digital ;24 cm.
內容註:	"Chapter 1: Introduction" -- "Chapter 2: Student and Teacher Models in KD" -- "Chapter 3: Distilled Knowledge in KD" -- "Chapter 4: Application of KD in High-Level Vision Tasks" -- "Chapter 5: Application of KD in Low-Level Vision Tasks" -- "Chapter 6: Application of KD beyond Model Compression" -- "Chapter 7: Conclusion".
Contained By:	Springer Nature eBook
標題:	Computer vision. -
電子資源:	https://doi.org/10.1007/978-981-95-0367-4
ISBN:	9789819503674

Knowledge distillation in computer vision
Zhang, Linfeng.

Knowledge distillation in computer vision[electronic resource] /by Linfeng Zhang. - Singapore :Springer Nature Singapore :2025. - viii, 140 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

"Chapter 1: Introduction" -- "Chapter 2: Student and Teacher Models in KD" -- "Chapter 3: Distilled Knowledge in KD" -- "Chapter 4: Application of KD in High-Level Vision Tasks" -- "Chapter 5: Application of KD in Low-Level Vision Tasks" -- "Chapter 6: Application of KD beyond Model Compression" -- "Chapter 7: Conclusion".

Discover the cutting-edge advancements in knowledge distillation for computer vision within this comprehensive monograph. As neural networks become increasingly complex, the demand for efficient and lightweight models grows critical, especially for real-world applications. This book uniquely bridges the gap between academic research and industrial implementation, exploring innovative methods to compress and accelerate deep neural networks without sacrificing accuracy. It addresses two fundamental problems in knowledge distillation: constructing effective student and teacher models and selecting the appropriate knowledge to distill. Presenting groundbreaking research on self-distillation and task-irrelevant knowledge distillation, the book offers new perspectives on model optimization. Readers will gain insights into applying these techniques across a wide range of visual tasks, from 2D and 3D object detection to image generation, effectively bridging the gap between AI research and practical deployment. By engaging with this text, readers will learn to enhance model performance, reduce computational costs, and improve model robustness. This book is ideal for researchers, practitioners, and advanced students with a background in computer vision and deep learning. Equip yourself with the knowledge to design and implement knowledge distillation, thereby improving the efficiency of computer vision models.

ISBN: 9789819503674

Standard No.: 10.1007/978-981-95-0367-4doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Knowledge distillation in computer vision
LDR:02782nmm a2200337 a 4500 001 2422954
003 DE-He213
005 20260102122636.0
006 m d
007 cr nn 008maaau
008 260505s2025 si s 0 eng d
020 $a 9789819503674 $q (electronic bk.)
020 $a 9789819503667 $q (paper)
024 7 $a 10.1007/978-981-95-0367-4 $2 doi
035 $a 978-981-95-0367-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .Z63 2025
100 1 $a Zhang, Linfeng. $3 3805162
245 1 0 $a Knowledge distillation in computer vision $h [electronic resource] / $c by Linfeng Zhang.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a viii, 140 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5776
505 0 $a "Chapter 1: Introduction" -- "Chapter 2: Student and Teacher Models in KD" -- "Chapter 3: Distilled Knowledge in KD" -- "Chapter 4: Application of KD in High-Level Vision Tasks" -- "Chapter 5: Application of KD in Low-Level Vision Tasks" -- "Chapter 6: Application of KD beyond Model Compression" -- "Chapter 7: Conclusion".
520 $a Discover the cutting-edge advancements in knowledge distillation for computer vision within this comprehensive monograph. As neural networks become increasingly complex, the demand for efficient and lightweight models grows critical, especially for real-world applications. This book uniquely bridges the gap between academic research and industrial implementation, exploring innovative methods to compress and accelerate deep neural networks without sacrificing accuracy. It addresses two fundamental problems in knowledge distillation: constructing effective student and teacher models and selecting the appropriate knowledge to distill. Presenting groundbreaking research on self-distillation and task-irrelevant knowledge distillation, the book offers new perspectives on model optimization. Readers will gain insights into applying these techniques across a wide range of visual tasks, from 2D and 3D object detection to image generation, effectively bridging the gap between AI research and practical deployment. By engaging with this text, readers will learn to enhance model performance, reduce computational costs, and improve model robustness. This book is ideal for researchers, practitioners, and advanced students with a background in computer vision and deep learning. Equip yourself with the knowledge to design and implement knowledge distillation, thereby improving the efficiency of computer vision models.
650 0 $a Computer vision. $3 540671
650 1 4 $a Computer Vision. $3 3538524
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
650 2 4 $a Artificial Intelligence. $3 769149
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 1567571
856 4 0 $u https://doi.org/10.1007/978-981-95-0367-4
950 $a Professional and Applied Computing (SpringerNature-12059)