Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Venkat Devarajan


Requirement of significant amount of labeled training data is a major drawback in training deep neural networks (DNNs), due to the presence of mislabeled examples in these datasets. This label noise is shown to have an adverse effect on the generalization performance of DNNs. Thus, reducing the consequences of label noise is of much research value. In this dissertation, we focus on improving our understanding of label noise and, achieving better generalization performance. Due to the lack of ground truth with real world noisy datasets, most researchers create synthetic noisy datasets to develop robust training methods. Among these methods, stopping the training in the early stages is shown to achieve better generalization performance with label noise. However, identifying such training stop point without ground truth is a demanding problem. We propose novel training methods to identify training stop point when noise rate is known and unknown. We further identify that the significance of stopping the training in the early stages and the effectiveness of several existing training methods are reduced with complex label noisy datasets. Thus, complex realistic noisy datasets that additionally provide ground truth are necessary to study and develop robust training methods. Therefore, we propose novel Pseudo noisy datasets that resemble complex noisy datasets.


Label noise, Deep neural networks


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington