Author ORCID Identifier

0000-0001-8948-8011

Date of Award

5-2-2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Rolando Estrada, Ph.D.

Second Advisor

Ashwin Ashok, Ph.D.

Abstract

Machine Learning requires data. Without the availability of large, high-quality datasets, the success of deep learning in recent years would not have been possible. Data is the fundamental building block in developing AI pipelines. However, due to the limitations in measurement tools, lack of control and immutability of real-life datasets, the general approach to developing machine learning solutions has evolved to be model-centric. This Dissertation explores the possibility of Data-centric AI by looking at the development of a novel technology | flexible photorealistic simulations | that can generate labeled datasets for use in lieu of real data in various fields of deep-learning accelerated computer vision. In each chapter of this work, we'll follow a major phase shift that represents a forward step in the applications of this field. From proof of concept, Improving existing methods, Applications on hard tasks, to achieving state-of-the-art performance.

DOI

https://doi.org/10.57709/28830665

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Available for download on Tuesday, October 25, 2022

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