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Explainable Deep Learning Ai : Methods and Challenges
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Explainable Deep Learning AI: Methods and Challenges
Language: English
Published by Academic Press 2023-02-24, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Explainable Deep Learning AI: Methods and Challenges
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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
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3d Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods
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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
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Explainable Deep Learning Ai : Methods and Challenges
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Explainable Deep Learning AI: Methods and Challenges
Language: English
Published by Elsevier Science & Technology, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Explainable Deep Learning AI: Methods and Challenges
Language: English
Published by Elsevier Science & Technology, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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3d Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods
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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
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Explainable Deep Learning AI: Methods and Challenges
Language: English
Published by Elsevier Science & Technology, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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3d Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
Language: English
Published by Springer Nature Switzerland AG, 2022
ISBN 10: 3030891828 ISBN 13: 9783030891824
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3d Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
Language: English
Published by Springer-Nature New York Inc, 2021
ISBN 10: 3030891798 ISBN 13: 9783030891794
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Add to basketHardcover. Condition: Brand New. 160 pages. 9.25x6.10x0.55 inches. In Stock.
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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods
Language: English
Published by Springer International Publishing, 2022
ISBN 10: 3030891828 ISBN 13: 9783030891824
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding.With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods.A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research.Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.
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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods
Language: English
Published by Springer International Publishing, 2021
ISBN 10: 3030891798 ISBN 13: 9783030891794
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding.With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods.A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research.Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.
-
Explainable Deep Learning AI : Methods and Challenges
Language: English
Published by Elsevier Science & Technology Feb 2023, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
Taschenbuch. Condition: Neu. Neuware - Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
