Overview
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
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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Table of contents (11 chapters)
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Front Matter
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Notions and Concepts on Explainability and Interpretability
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Front Matter
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Explainability and Interpretability in Machine Learning
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Front Matter
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Explainability and Interpretability in Computer Vision
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Front Matter
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Explainability and Interpretability in First Impressions Analysis
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Front Matter
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Keywords
- Explainable models in computer vision
- Explainable learning machines
- Interpretable models
- Explaining human behavior from data
- Interpreting human behavior analysis models
- Explaining first impressions
- Job candidate screening
- Multimodal analysis of human behavior
- Explaining Looking at people
- Chalearn looking at people challenges
- Explainable and interpretable decision support systems
- Benchmarking of explainable and interpretable models
About this book
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
Editors and Affiliations
Bibliographic Information
Book Title: Explainable and Interpretable Models in Computer Vision and Machine Learning
Editors: Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, Umut Güçlü, Marcel van Gerven
Series Title: The Springer Series on Challenges in Machine Learning
DOI: https://doi.org/10.1007/978-3-319-98131-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-98130-7Published: 16 January 2019
eBook ISBN: 978-3-319-98131-4Published: 29 November 2018
Series ISSN: 2520-131X
Series E-ISSN: 2520-1328
Edition Number: 1
Number of Pages: XVII, 299
Number of Illustrations: 15 b/w illustrations, 58 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition