Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/120659
Type: | Thesis |
Title: | Efficient Deep Learning Models with Autoencoder Regularization and Information Bottleneck Compression |
Author: | Williams, Jerome Oskar |
Issue Date: | 2019 |
School/Discipline: | School of Computer Science |
Abstract: | Improving efficiency in deep learning models implies achieving a more accurate model for a given computational budget, or conversely a faster, leaner model without losing accuracy. In order to improve efficiency, we can use regularization to to improve generalization to the real world, and compression to improve speed. Due to the information-restricting nature of regularization, these two methods are related. Firstly we present a novel autoencoder architecture as a method of regularization for Pedestrian Detection. Secondly, we present a hyperparameter-free, iterative compression method based on measuring the information content of the model with the Information Bottleneck principle. |
Advisor: | Carneiro, Gustavo Suter, David Sasdelli, Michele |
Dissertation Note: | Thesis (MPhil) -- University of Adelaide, School of Computer Science, 2019 |
Keywords: | Machine learning neural network deep learning computer vision regularization compression information bottleneck autoencoder pedestrian detection region of interest convolutional statistics efficiency |
Provenance: | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals |
Appears in Collections: | Research Theses |
Files in This Item:
File | Description | Size | Format | |
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Williams2019_MPhil.pdf | Thesis | 1.98 MB | Adobe PDF | View/Open |
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