000			02371nam a22003255i 4500
001			91066
005			20240705153642.0
010			_a978-3-030-45529-3 _dcompra
090			_a91066
100			_a20231023d2020 k||y0pory50 ba
101	0		_aeng
102			_aCH
200	1		_aDomain adaptation in computer vision with deep learning _bDocumento eletrónico _fedited by Hemanth Venkateswara, Sethuraman Panchanathan
210			_aCham _cSpringer International Publishing _d2020
215			_aXI, 256 p. _cil.
303			_aThis 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.
606			_aMachine learning
606			_aImage processing _xDigital techniques
606			_aComputer vision
606			_aSignal processing
606			_aArtificial intelligence
606			_aApplication software
680			_aQ325.5-.7
702			_aVenkateswara _bHemanth _4340 _974081
702			_aPanchanathan _bSethuraman _4340 _974082
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-45529-3
942			_2lcc _cF _n0