Abstract
Deep learning systems have brought about a paradigm shift in the field of artificial intelligence, often surpassing human performance across a wide range of tasks. Nonetheless, there are numerous apprehensions regarding their robustness when deployed in the real world. Multiple studies have demonstrated that deep learning models tend to latch onto biases present in their training data instead of truly solving the tasks. Given the pervasive nature of this issue across various datasets and tasks, researchers have proposed a variety of techniques to improve bias resilience. However, the evaluation protocols used in prior works leave many open questions regarding their true robustness and the primary goal of this dissertation is to explore these questions. Specifically, we conduct studies with more comprehensive evaluation protocols to study if the systems are right for the right reasons and generalize to realistic forms of biases. Apart from such investigations, we also make progress in method development, by developing methods that focus on simplicity over prior methods, meanwhile remaining on par with or even surpassing the state-of-the-art. Overall, the dissertation makes progress toward addressing issues in developing bias-resilient systems and delineates potential directions for future research in the field.
Library of Congress Subject Headings
Neural networks (Computer science); Deep learning (Machine learning); Robust control
Publication Date
12-8-2023
Document Type
Dissertation
Student Type
Graduate
Degree Name
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science
College
College of Science
Advisor
Christopher Kanan
Advisor/Committee Member
Qi Yu
Advisor/Committee Member
Nathan Cahill
Recommended Citation
Shrestha, Robik, "Towards Bias-Resilient Deep Neural Networks" (2023). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11657
Campus
RIT – Main Campus
Plan Codes
IMGS-PHD