Explainable Deep Learning AI
Methods and Challenges
Published on 24. February 2023
Book
Paperback/Softback
346 pages
978-0-323-96098-4 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
This book is intended for researchers, PhD students, and practitioners in the area of Explainable Artificial Intelligence (XAI) specifically related to Deep Learning AI Methods
Dimensions
Height: 189 mm
Width: 234 mm
Thickness: 23 mm
Weight
698 gr
ISBN-13
978-0-323-96098-4 (9780323960984)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jenny Benois-Pineau | Romain Bourqui | Dragutin Petkovic
Explainable Deep Learning AI
Methods and Challenges
E-Book
02/2023
Academic Press
€113.00
Available for download
Persons
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 Universite 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.
Editor
Professor, Labri/University Bordeaux, France
Associate Professor, Labri/University Bordeaux, France
Professor, Computer Science department, San Francisco State University, USA
Senior Researcher, Laboratory of Informatics of Grenoble and Multimedia Information Indexing and Retrieval Group, leader of the MRIM group, CNRS, France
Content
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
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