Deep learning techniques in data augmentation and neural network design

Abstract

In recent years, deep learning has revolutionized computer vision and has been applied to a range of problems where it often achieves accuracies equal to or greater than those obtainable by individual human experts. This research improves on the state-of-the-art by proposing, implementing, and testing new models, architectures, and training methods that are more efficient while maintaining or improving the accuracy of previous methods. Special attention is focused on improvements that facilitate the specific needs of resource-constrained devices such as smartphones, and embedded systems, and in cases where obtaining sufficient data is difficult. For this reason, the topic of data augmentation is a major theme of this work.

Due to the ever greater need for smarter embedded devices, my research has focused on novel network designs and data augmentation techniques for a wide range of diverse tasks, connected only by the need for more efficient architectures and more data – in many cases improving the accuracy over previous works in the process.