Active one-shot learning for personalized human affect estimation
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119771 |
| _version_ | 1811097050786299904 |
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| author | Xu, Jacqueline L |
| author2 | Ognjen Rudovic and Rosalind Picard. |
| author_facet | Ognjen Rudovic and Rosalind Picard. Xu, Jacqueline L |
| author_sort | Xu, Jacqueline L |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T16:53:32Z |
| format | Thesis |
| id | mit-1721.1/119771 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:53:32Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197712019-04-12T22:59:14Z Active one-shot learning for personalized human affect estimation Xu, Jacqueline L Ognjen Rudovic and Rosalind Picard. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-46). Building models that can classify human affect leads to the challenge of learning on data that is complex in features and limited in size and labels. How can these models balance being general and personalized, capturing both the commonalities and the individual quirks of people? While previous research has explored the intersection of deep learning, active learning, and one-shot learning to craft models that are semi-supervised and data-efficient, these methods have not yet been examined in the context of personalized affective computing. This study presents a novel active one-shot learning model for personalized estimation of human affect, in particular, detection of pain from facial expressions. The model demonstrates the ability to learn an active learner that achieves high accuracy, learns to become data efficient, and introduces model personalization to match or outperform fully supervised and population-level models. by Jacqueline L. Xu. M. Eng. 2018-12-18T20:03:58Z 2018-12-18T20:03:58Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119771 1078150914 eng 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 46 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Xu, Jacqueline L Active one-shot learning for personalized human affect estimation |
| title | Active one-shot learning for personalized human affect estimation |
| title_full | Active one-shot learning for personalized human affect estimation |
| title_fullStr | Active one-shot learning for personalized human affect estimation |
| title_full_unstemmed | Active one-shot learning for personalized human affect estimation |
| title_short | Active one-shot learning for personalized human affect estimation |
| title_sort | active one shot learning for personalized human affect estimation |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119771 |
| work_keys_str_mv | AT xujacquelinel activeoneshotlearningforpersonalizedhumanaffectestimation |