Explainable Deep Learning AI: Methods and Challenges
- Length: 346 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2023-03-10
- ISBN-10: 0323960987
- ISBN-13: 9780323960984
- Sales Rank: #679979 (See Top 100 Books)
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.
Cover image Title page Table of Contents Copyright List of contributors Preface Chapter 1: Introduction Abstract Chapter 2: Explainable deep learning: concepts, methods, and new developments Abstract Acknowledgement 2.1. Introduction 2.2. Concepts 2.3. Methods 2.4. New developments 2.5. Limitations and future work References Chapter 3: Compact visualization of DNN classification performances for interpretation and improvement Abstract Acknowledgements 3.1. Introduction 3.2. Previous works 3.3. Proposed method for compact visualization of DNN classification performances 3.4. Experimental protocol 3.5. Results and discussion 3.6. Conclusion References Chapter 4: Characterizing a scene recognition model by identifying the effect of input features via semantic-wise attribution Abstract Acknowledgements 4.1. Introduction 4.2. Semantic-wise attribution 4.3. Experimental results 4.4. Conclusions References Chapter 5: A feature understanding method for explanation of image classification by convolutional neural networks Abstract Acknowledgements 5.1. Introduction 5.2. Principles of white-box explanation methods 5.3. Explanation methods 5.4. The proposed improvement – modified FEM 5.5. Experimental results 5.6. Conclusion References Chapter 6: Explainable deep learning for decrypting disease signatures in multiple sclerosis Abstract Acknowledgements 6.1. Introduction 6.2. State-of-the-art 6.3. Materials and methods 6.4. Results 6.5. Discussion 6.6. Conclusions References Chapter 7: Explanation of CNN image classifiers with hiding parts Abstract Acknowledgements 7.1. Introduction 7.2. Explanation methods 7.3. Recursive division approach 7.4. Quality of the model 7.5. Experimental modeling 7.6. Conclusion References Chapter 8: Remove to improve? Abstract Glossary Acknowledgements 8.1. Introduction 8.2. Previous work 8.3. Definitions 8.4. Experiments 8.5. Model selection 8.6. Discussion and conclusions References Chapter 9: Explaining CNN classifier using association rule mining methods on time-series Abstract 9.1. Introduction 9.2. Related work 9.3. Background 9.4. Methods 9.5. Evaluation metrics 9.6. Experimental results 9.7. Conclusion and future work References Chapter 10: A methodology to compare XAI explanations on natural language processing Abstract Glossary 10.1. Introduction 10.2. Related works 10.3. Generating explanations 10.4. Evaluation without end users 10.5. Psychometric user study 10.6. Conclusion References Chapter 11: Improving malware detection with explainable machine learning Abstract 11.1. Introduction 11.2. Background 11.3. Explanation methods 11.4. Explaining Android ransomware 11.5. Experimental analysis 11.6. Discussion 11.7. Conclusion References Chapter 12: Explainability in medical image captioning Abstract Acknowledgement 12.1. Introduction 12.2. Related work 12.3. Methodology 12.4. Experimental results 12.5. Conclusion and future work References Chapter 13: User tests & techniques for the post-hoc explanation of deep learning Abstract Acknowledgements 13.1. Introduction 13.2. Post-hoc explanations using factual examples 13.3. Counterfactual & semifactual explanations: images 13.4. Contrastive explanations: time series 13.5. User studies on contrastive explanations 13.6. Conclusions References Chapter 14: Theoretical analysis of LIME Abstract Acknowledgements 14.1. Introduction 14.2. LIME for images 14.3. LIME for text data 14.4. LIME for tabular data 14.5. Conclusion References Chapter 15: Conclusion Abstract Index
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