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Explanation-driven learning-based models for visual recognition tasks
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Daniels, Zachary. Explanation-driven learning-based models for visual recognition tasks. Retrieved from https://doi.org/doi:10.7282/t3-8w7j-1v31

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TitleExplanation-driven learning-based models for visual recognition tasks
NameDaniels, Zachary (author); Metaxas, Dimitris N (chair); Michmizos, Konstantinos (internal member); Moustakides, George (internal member); Li, Fuxin (outside member); Rutgers University; School of Graduate Studies
Date Created2020
Other Date2020-10 (degree)
SubjectComputer vision, Computer Science
Extent1 online resource (xxiii, 210 pages)
DescriptionSafety-critical applications (e.g., autonomous vehicles, human-machine teaming, and automated medical diagnosis) often require the use of computational agents that are capable of understanding and reasoning about the high-level content of real-world scene images in order to make rational and grounded decisions that can be trusted by humans. Many of these agents rely on machine learning-based models which are increasingly being treated as black-boxes. One way to increase model interpretability is to make explainability a core principle of the model, e.g., by forcing deep neural networks to explicitly learn grounded and interpretable features. In this thesis, I provide a high-level overview of the field of explainable/interpretable machine learning and review some existing approaches for interpreting neural networks used for computer vision tasks. I also introduce four novel approaches for making convolutional neural networks (CNNs) more interpretable by utilizing explainability as a guiding principle when designing the model architecture. Finally, I discuss some possible future research directions involving explanation-driven machine learning.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-8w7j-1v31
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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