Past Events

PhD Defense

Explanation-Driven Learning-Based Models for Visual Recognition Tasks


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Friday, April 24, 2020, 11:00am - 12:30pm


Speaker: Zachary Daniels

Location : Remote via Webex


Prof. Dimitris Metaxas (Chair)

Prof. Konstantinos Michmizos

Prof. George Moustakides

Prof. Fuxin Li (External Member, Oregon State University)

Event Type: PhD Defense

Abstract: Safety-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. This talk will consist of three parts. First, I will 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. Second, I will introduce several novel approaches for making convolutional neural networks (CNNs) more interpretable by utilizing explainability as a guiding principle when designing the model architecture. Third, I will discuss some possible future research directions involving explanation-driven machine learning.



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