Explainable Deep Learning AI (eBook, ePUB)

Methods and Challenges
Redaktion: Benois-Pineau, Jenny; Quenot, Georges; Petkovic, Dragutin; Bourqui, Romain

• Format: ePub

Produktbeschreibung

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.

• 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 346
• Erscheinungstermin: 20. Februar 2023
• Englisch
• ISBN-13: 9780323993883
• Artikelnr.: 67490487

Inhaltsangabe

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