Browsing University of Waterloo by Subject "explainable AI"
Now showing items 1-6 of 6
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AdvEx: Interactive Visual Explorations of Adversarial Evasion Attacks
(University of Waterloo, 2023-06-28)Adversarial machine learning (AML) focuses on studying attacks that can fool machine learning algorithms into generating incorrect outcomes as well as the defenses against worst-case attacks to strengthen the adversarial ... -
Class Based Strategies for Understanding Neural Networks
(University of Waterloo, 2020-02-07)One of the main challenges for broad adoption of deep learning based models such as Convolutional Neural Networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model ... -
Diabetic retinopathy grading with respect to the segmented lesions
(University of Waterloo, 2022-05-19)One of the leading causes of irreversible vision loss is Diabetic Retinopathy (DR). The International Clinical Diabetic Retinopathy scale (ICDRS) provides grading criteria for DR. Deep Convolutional Neural Networks (DCNNs) ... -
Towards Explainable Generative Adversarial Networks
(University of Waterloo, 2022-05-09)As Generative Adversarial Networks become more and more popular for sample generation, the demand for human interpretable explanations have also skyrocketed. With the rising popularity of Generative Adversarial Networks ... -
User-specific explanations of AI systems attuned to psychological profiles: a user study
(University of Waterloo, 2023-05-24)In this thesis, we design a model aimed at supporting user-specific explanations from AI systems and present the results of a user study conducted to determine whether the algorithms used to attune the output to the user ... -
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
(University of Waterloo, 2022-01-18)Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer to be useful. In this thesis, we aim to ...