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Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao
Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao
All Dissertations
Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …
Machine Learning Classifiers For Chronic Obstructive Pulmonary Disease Assessment Using Lung Ct Data., Halimah Alsurayhi
Machine Learning Classifiers For Chronic Obstructive Pulmonary Disease Assessment Using Lung Ct Data., Halimah Alsurayhi
Electronic Thesis and Dissertation Repository
Chronic Obstructive Pulmonary Disease (COPD) is a condition characterized by persistent inflammation and airflow blockages in the lungs, contributing to a significant number of deaths globally each year. To guide tailored treatment strategies and mitigate future risks, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) employs a multifaceted assessment system of COPD severity, considering patient's lung function, symptoms, and exacerbation history. COPD staging systems, such as the high-resolution eight-stage COPD system and the GOLD 2023 three staging systems, have been later developed based on these factors. Lung Computed Tomography (CT) is becoming increasingly crucial in investigating COPD as it …
Active Uncertainty Representation Learning: Toward More Label Efficiency In Deep Learning, Salman Mohamadi
Active Uncertainty Representation Learning: Toward More Label Efficiency In Deep Learning, Salman Mohamadi
Graduate Theses, Dissertations, and Problem Reports
The primary goal of this dissertation is to investigate and improve the efficiency of deep learning algorithms, especially within computer vision problem domains, from the perspective of label-efficiency. This investigation showed that deep learning algorithms are mostly notorious for the lack of uncertainty representation. Accordingly, we aimed to develop an array of deep learning frameworks rich with uncertainty representation. These frameworks are mainly within two current pillars of machine learning, deep active learning and self-supervised learning. These frameworks include deep active ensemble sampling for efficient sample selection within deep active learning, a two-stage ensemble-based general self-training approach for existing visual …