Contrasting contrastive and supervised models interpretability
Name
1251801865-MIT.pdf
Size
60.38 MB
Format
Adobe PDF
Checksum (MD5)
554b66da50984070987fe65fc7a33490
Author(s)
You, Yejin.
Advisor(s)
Antonio Torralba and David Bau.
Date Issued
2021
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we compare the representations of an unsupervised contrastive model to those of an equivalent supervised model using several deep neural network interpretability methods: network dissection, sparsity experiments, and saliency maps. Network dissections of self-supervised contrastive and supervised models show that the neurons of the contrastive model tend to learn about different parts of an object (ie. top-half of a dog or left-half of a person) while the neurons of the supervised model tend to learn about the entire object (ie. a dog or a person). Sparsity experiments show that the representations learned by the contrastive model are less sparse than the representations learned by the supervised counterpart model. Saliency maps show that the contrastive model focuses more on specific parts of the input image. Finally, we find that the contrastive model representations transfer better to finegrained classification tasks than the supervised model representations.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 61-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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