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Explainable Deep Learning AI Methods and Challenges

Benois-Pineau Jenny Petkovic Dragutin Quenot Georges
Heftet / 2023 / Engelsk

Produktdetaljer

ISBN
9780323960984
Publisert
2023-02-24
Utgiver
Vendor
Academic Press Inc
Vekt
720 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
346

Redaktør
Benois-Pineau Jenny
Petkovic Dragutin
Quenot Georges

Biographical note

Jenny Benois-Pineau is a professor of computer science at the University of Bordeaux and head of the “Video Analysis and Indexing” research group of the “Image and Sound” team of LABRI UMR 58000 Université Bordeaux / CNRS / IPB-ENSEIRB. She was deputy scientific director of theme B of the French national research unit CNRS GDR ISIS (2008-2015) and is currently in charge of international relations at the College of Sciences and Technologies of the University of Bordeaux. She obtained her doctorate in Signals and Systems in Moscow and her Habilitation to Direct Research in Computer Science and Image Processing at the University of Nantes in France. Her subjects of interest include image and video analysis and indexing, artificial intelligence methods applied to image recognition. Since 2009 he’s been an Associate Professor in the Computer Science Department of the IUT ("Technical School"), University of Bordeaux (Talence), France. He is also deputy director of the BKB ("Bench to Knowledge and Beyond") team of LaBRI. Dragutin Petkovic is Professor in the Computer Science department at San Francisco State University, USA. Senior researcher at CNRS, leader of the MRIM group. Works at the Laboratory of Informatics of Grenoble and Multimedia Information Indexing and Retrieval Group.

Explainable Deep Learning AI Methods and Challenges

Benois-Pineau Jenny Petkovic Dragutin Quenot Georges
Heftet / 2023 / Engelsk
Benois-Pineau Jenny Petkovic Dragutin Quenot Georges
Heftet / 2023 / Engelsk
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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.
Les mer
1. Introduction 2. Explainable Deep Learning: Methods, Concepts and New Developments 3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement 4. Explaining How Deep Neural Networks Forget by Deep Visualization 5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution 6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks 7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis 8. Explanation of CNN Image Classifiers with Hiding Parts 9. Remove to Improve? 10. Explaining CNN classifier using Association Rule Mining Methods on time-series 11. A Methodology to compare XAI Explanations on Natural Language Processing 12. Improving Malware Detection with Explainable Machine Learning 13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing 14. Explainable Deep Learning for Multimedia Indexing and Retrieval 15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models 16. Conclusion
Les mer
A complete reference on the methods of Explainable Artificial Intelligence (XAI) in Deep Learning and their cutting-edge application
Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI Explores the latest developments in general XAI methods for Deep Learning Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
Les mer

Relaterte produkter

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.
Les mer
1. Introduction 2. Explainable Deep Learning: Methods, Concepts and New Developments 3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement 4. Explaining How Deep Neural Networks Forget by Deep Visualization 5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution 6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks 7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis 8. Explanation of CNN Image Classifiers with Hiding Parts 9. Remove to Improve? 10. Explaining CNN classifier using Association Rule Mining Methods on time-series 11. A Methodology to compare XAI Explanations on Natural Language Processing 12. Improving Malware Detection with Explainable Machine Learning 13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing 14. Explainable Deep Learning for Multimedia Indexing and Retrieval 15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models 16. Conclusion
Les mer
A complete reference on the methods of Explainable Artificial Intelligence (XAI) in Deep Learning and their cutting-edge application
Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI Explores the latest developments in general XAI methods for Deep Learning Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
Les mer

Produktdetaljer

ISBN
9780323960984
Publisert
2023-02-24
Utgiver
Vendor
Academic Press Inc
Vekt
720 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
346

Redaktør
Benois-Pineau Jenny
Petkovic Dragutin
Quenot Georges

Biographical note

Jenny Benois-Pineau is a professor of computer science at the University of Bordeaux and head of the “Video Analysis and Indexing” research group of the “Image and Sound” team of LABRI UMR 58000 Université Bordeaux / CNRS / IPB-ENSEIRB. She was deputy scientific director of theme B of the French national research unit CNRS GDR ISIS (2008-2015) and is currently in charge of international relations at the College of Sciences and Technologies of the University of Bordeaux. She obtained her doctorate in Signals and Systems in Moscow and her Habilitation to Direct Research in Computer Science and Image Processing at the University of Nantes in France. Her subjects of interest include image and video analysis and indexing, artificial intelligence methods applied to image recognition. Since 2009 he’s been an Associate Professor in the Computer Science Department of the IUT ("Technical School"), University of Bordeaux (Talence), France. He is also deputy director of the BKB ("Bench to Knowledge and Beyond") team of LaBRI. Dragutin Petkovic is Professor in the Computer Science department at San Francisco State University, USA. Senior researcher at CNRS, leader of the MRIM group. Works at the Laboratory of Informatics of Grenoble and Multimedia Information Indexing and Retrieval Group.

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