Invariance properties of the human visual system in one-shot learning
Name
1066694254-MIT.pdf
Description
Full printable version
Size
4.67 MB
Format
Adobe PDF
Checksum (MD5)
5668ba47a2fdba93a540ad39dcf11e60
Author(s)
Han, Yena
Advisor(s)
Tomaso Poggio.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
This work first characterizes human invariant recognition in one-shot learning. By using novel stimuli, we address the question whether invariance to transformation emerges from experience and memorization of templates or from the brain instantly computing invariant representation. Our psychophysical experimental results suggest that human vision produces a representation that is robust in terms of scale change, but it needs experience for translation-invariance. Next, we examine the implication of the experimental data with regards to computational modeling. In particular, we confirm that the eccentricity-dependent model [16], where scale-invariance is built in the underlying architecture, reproduces the human data closely.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 45-46).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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