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Generative Adversarial Network : Principle and Practice / by Peng Long, Xiaozhou Guo

Von: Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Singapore : Springer Nature Singapore, 2026Verlag: Singapore : Imprint: Springer, 2026Auflage: 1st ed. 2026Beschreibung: 1 Online-Ressource(XI, 436 p. 265 illus., 221 illus. in color.)ISBN:
  • 9789819694044
Schlagwörter: Andere physische Formen: 9789819694037 | 9789819694051 | 9789819694068 | Erscheint auch als: 9789819694037 Druck-Ausgabe | Erscheint auch als: 9789819694051 Druck-Ausgabe | Erscheint auch als: 9789819694068 Druck-AusgabeDDC-Klassifikation:
  • 006.37 23
DOI: DOI: 10.1007/978-981-96-9404-4Online-Ressourcen: Zusammenfassung: "Chapter 1 Generative Model" -- "Chapter 2 Objective Function Optimization" -- "Chapter 3 Training Techniques" -- "Chapter 4 Evaluation Methods and Visualization" -- "Chapter 5 Image Generation" -- "Chapter 6 Image Translation" -- "Chapter 7 Face Image Editing" -- "Chapter 8 Image Quality Enhancement" -- "Chapter 9 Generation of 3D pictures and videos" -- "Chapter 10 General Image Editing" -- "Chapter 11 Adversarial Attack" -- "Chapter 12 Speech Signal Processing".Zusammenfassung: This book comprehensively and systematically introduces the theory of generative adversarial networks and its applications in image and voice processing. This book consists of 12 chapters, of which the first four chapters present basic knowledge, including the principle of GAN, optimization objectives, training methods and evaluation indicators. The last eight chapters introduce the applications of GAN in various vertical fields, covering image generation, video generation, image translation, face image editing, image quality improvement, general image editing, anti-attack, voice signal processing and other fields. Through reading this book, readers will thoroughly understand the principles of GAN, various GAN model designs, and learn to apply GAN for most vision and voice tasks. This book is suitable for the junior researchers, students or industrial practitioners in related areas.PPN: PPN: 1960499181Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS
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