Scalable large scale visual recognition using multi-label image classification
Name
1129456299-MIT.pdf
Size
1.18 MB
Format
Adobe PDF
Checksum (MD5)
949fa3cb5748d5c4253322ec2748b92a
Author(s)
Huang, Aaron R.
Advisor(s)
Fredo Durand.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Today, the amount of image categories and labels for visual recognition is growing at an astonishing rate, and it is becoming increasingly impractical to keep up a high level of accuracy across all categories in addition to retraining these deep networks to classify all these new labels along with previous ones. However, we note that the majority of new labels that are being added now are simply subsets and combinations of existing labels, just an extra step of specificity. In this study, we will be looking at creating a model that can be trained on traditional datasets, either single-class or multi-class, but then can be quickly adapted and trained on new multi-class image datasets, in which the class components are part of the original training set, without losing accuracy on the original dataset and not having to train on it either.
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 50-51).
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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