MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Scalable large scale visual recognition using multi-label image classification

Author(s)
Huang, Aaron R.
Thumbnail
Download1129456299-MIT.pdf (1.179Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Fredo Durand.
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. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
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).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/123173
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Electrical Engineering and Computer Sciences - Master's degree
  • Electrical Engineering and Computer Sciences - Master's degree

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Facebook Instagram YouTube RSS

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.