Springer Domain Adaptation in Computer Vision with Deep Learning

Product Description 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. From the Back Cover 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. About the Author Hemanth Venkateswara is an Assistant Research Professor at the School of Computing Informatics and Decision Systems Engineering at Arizona State University. He completed his PhD in machine learning and computer vision in 2017 from Arizona State University. Hemanth’s research interests include transfer learning, active learning, zero-shot learning, incremental learning and generative models using deep learning. His research explores knowledge transfer paradigms for deep neural networks that are challenging to train due to paucity of annotated data. Hemanth holds a bachelor’s degree in Physics and master’s degrees in Physics and Computer Science. Prior to his PhD, Hemanth worked as a senior software engineer at Alcatel-Lucent Technologies, India. Hemanth is a member of the IEEE and the ACM. Sethuraman “Panch” Panchanathan leads the knowledge enterprise at Arizona State University, which advances research, innovation, strategic partnerships, entrepreneurship, global and economic development at ASU. He is the Director of the Center for Cognitive Ubiquitous Computing at ASU. Panchanathan’s research interests are in the areas of human-centered multimedia computing, haptic user