Greedy layerwise training of convolutional neural networks
Name
1128279897-MIT.pdf
Size
1.57 MB
Format
Adobe PDF
Checksum (MD5)
49c8b0e9f23de81371418c9606960d57
Author(s)
Trinh, Loc Quang.
Advisor(s)
Aleksander Ma̧dry.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Layerwise training presents an alternative approach to end-to-end back-propagation for training deep convolutional neural networks. Although previous work was unsuccessful in demonstrating the viability of layerwise training, especially on large-scale datasets such as ImageNet, recent work has shown that layerwise training on specific architectures can yield highly competitive performances. On ImageNet, the layerwise trained networks can perform comparably to many state-of-the-art end-to-end trained networks. In this thesis, we compare the performance gap between the two training procedures across a wide range of network architectures and further analyze the possible limitations of layerwise training. Our results show that layerwise training quickly saturates after a certain critical layer, due to the overfitting of early layers within the networks. We discuss several approaches we took to address this issue and help layerwise training improve across multiple architectures. From a fundamental standpoint, this study emphasizes the need to open the blackbox that is modern deep neural networks and investigate the layerwise interactions between intermediate hidden layers within deep networks, all through the lens of layerwise training.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-63).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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