Domain Adaptation in Computer Vision with Deep Learning

Name: Domain Adaptation in Computer Vision with Deep Learning
Brand: Springer Nature Switzerland AG
SKU: 9783030455316
Price: 149.39 EUR
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Autor: Hemanth Venkateswara

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer Nature Switzerland AG

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This book provides a survey of deep learning approaches to domain adaptation in computer vision. It...

Informácie o knihe

Autor

Hemanth Venkateswara

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2021

Stránok

268

EAN

9783030455316

ISBN

3030455319

Enbook ID

37021196

Vydavateľ

Springer Nature Switzerland AG

Hmotnosť

411

Rozmery

155 x 235 x 15

Kompletný popis

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

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Domain Adaptation in Computer Vision with Deep Learning

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