Portland State University. Department of Computer Science
Term of Graduation
Date of Publication
Doctor of Philosophy (Ph.D.) in Computer Science
1 online reource (viii, 109 pages)
While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when compared to human performance. Specifically, human vision is largely robust to noise and distortions, whereas deep learning performance tends to be brittle to modifications of test images, including being susceptible to adversarial examples. Additionally, deep-learning methods typically require very large collections of training examples for good performance on a task, whereas humans can learn to perform the same task with a much smaller number of training examples.
In this dissertation, I investigate whether the use of a biologically informed, unsupervised sparse coding algorithm can help to alleviate these shortcomings within classification networks. I find that (1) the non-linear encoding scheme of convolutional sparse coding, as opposed to the dictionary learned, contributes to classification performance when used within a model. In addition, (2) sparse coding helps classification models trained on clean images to be more robust to adversarial examples and images corrupted with high frequency noise. Finally, (3) sparse coding helps alleviate the number of human-annotated training labels needed for classification on stereo-video data. Overall, using unsupervised sparse coding within supervised models can help alleviate various shortcomings of traditional deep neural networks.
© 2020 Sheng Y. Lundquist
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Lundquist, Sheng Y., "Exploring the Potential of Sparse Coding for Machine Learning" (2020). Dissertations and Theses. Paper 5612.