Explainable and Interpretable Models in Computer Vision and Machine Learning

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<div><p>This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.</p><p></p><p>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.  </p><p> This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:</p><p> </p><p>·     Evaluation and Generalization in Interpretable Machine Learning</p><p>·         Explanation Methods in Deep Learning</p><p>·         Learning Functional Causal Models with Generative Neural Networks</p><p>·         Learning Interpreatable Rules for Multi-Label Classification</p><p>·         Structuring Neural Networks for More Explainable Predictions</p><p>·         Generating Post Hoc Rationales of Deep Visual Classification Decisions</p><p>·         Ensembling Visual Explanations</p><p>·         Explainable Deep Driving by Visualizing Causal Attention</p><p>·         Interdisciplinary Perspective on Algorithmic Job Candidate Search</p><p>·         Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions</p><p>·         Inherent Explainability Pattern Theory-based Video Event Interpretations</p></div><div><br></div>

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