This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Insufficient training data poses significant challenges to training a deep convolutional neural network (CNN) to solve a target task. One common solution to this problem is to use transfer learning with pre-trained networks to apply knowledge learned from one domain with sufficient data to a new domain with limited data…
Insufficient training data poses significant challenges to training a deep convolutional neural network (CNN) to solve a target task. One common solution to this problem is to use transfer learning with pre-trained networks to apply knowledge learned from one domain with sufficient data to a new domain with limited data and avoid training a deep network from scratch. However, for such methods to work in a transfer learning setting, learned features from the source domain need to be generalizable to the target domain, which is not guaranteed since the feature space and distributions of the source and target data may be different. This thesis aims to explore and understand the use of orthogonal convolutional neural networks to improve learning of diverse, generic features that are transferable to a novel task.
In this thesis, orthogonal regularization is used to pre-train deep CNNs to investigate if and how orthogonal convolution may improve feature extraction in transfer learning. Experiments using two limited medical image datasets in this thesis suggests that orthogonal regularization improves generality and reduces redundancy of learned features more effectively in certain deep networks for transfer learning. The results on feature selection and classification demonstrate the improvement in transferred features helps select more expressive features that improves generalization performance. To understand the effectiveness of orthogonal regularization on different architectures, this work studies the effects of residual learning on orthogonal convolution. Specifically, this work examines the presence of residual connections and its effects on feature similarities and show residual learning blocks help orthogonal convolution better preserve feature diversity across convolutional layers of a network and alleviate the increase in feature similarities caused by depth, demonstrating the importance of residual learning in making orthogonal convolution more effective.
