Explainable and Interpretable Models in Computer Vision and Machine Learning
Editors: Jair Escalante, H., Escalera, S., Guyon, I., Baró, X., Güçlütürk, Y., Güçlü, U., van Gerven, M.A.J. (Eds.)
Free Preview- Presents a snapshot of explainable and interpretable models in the context of computer vision and machine learning
- Covers fundamental topics to serve as a reference for newcomers to the field
- Offers successful methodologies, with applications of interest to the machine learning and computer vision communities
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- About this book
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This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.
This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:
· Evaluation and Generalization in Interpretable Machine Learning
· Explanation Methods in Deep Learning
· Learning Functional Causal Models with Generative Neural Networks
· Learning Interpreatable Rules for Multi-Label Classification
· Structuring Neural Networks for More Explainable Predictions
· Generating Post Hoc Rationales of Deep Visual Classification Decisions
· Ensembling Visual Explanations
· Explainable Deep Driving by Visualizing Causal Attention
· Interdisciplinary Perspective on Algorithmic Job Candidate Search
· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
· Inherent Explainability Pattern Theory-based Video Event Interpretations
- Table of contents (11 chapters)
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Considerations for Evaluation and Generalization in Interpretable Machine Learning
Pages 3-17
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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Pages 19-36
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Learning Functional Causal Models with Generative Neural Networks
Pages 39-80
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Learning Interpretable Rules for Multi-Label Classification
Pages 81-113
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Structuring Neural Networks for More Explainable Predictions
Pages 115-131
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Table of contents (11 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Explainable and Interpretable Models in Computer Vision and Machine Learning
- Editors
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- Hugo Jair Escalante
- Sergio Escalera
- Isabelle Guyon
- Xavier Baró
- Yağmur Güçlütürk
- Umut Güçlü
- Marcel A. J. van Gerven
- Series Title
- The Springer Series on Challenges in Machine Learning
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-319-98131-4
- DOI
- 10.1007/978-3-319-98131-4
- Hardcover ISBN
- 978-3-319-98130-7
- Series ISSN
- 2520-131X
- Edition Number
- 1
- Number of Pages
- XVII, 299
- Number of Illustrations
- 15 b/w illustrations, 58 illustrations in colour
- Topics
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