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Learning Perceptual Similarity from ...
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Wilber, Michael James.
Learning Perceptual Similarity from Crowds and Machines.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Learning Perceptual Similarity from Crowds and Machines./
Author:
Wilber, Michael James.
Description:
1 online resource (93 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:
Dissertation Abstracts International79-10B(E).
Subject:
Computer science. -
Online resource:
click for full text (PQDT)
ISBN:
9780438027343
Learning Perceptual Similarity from Crowds and Machines.
Wilber, Michael James.
Learning Perceptual Similarity from Crowds and Machines.
- 1 online resource (93 pages)
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Thesis (Ph.D.)--Cornell University, 2018.
Includes bibliographical references
How might we teach machine learning systems about what wine tastes like, or how to appreciate the similarities in different kinds of artwork?
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780438027343Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Learning Perceptual Similarity from Crowds and Machines.
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Learning Perceptual Similarity from Crowds and Machines.
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1 online resource (93 pages)
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Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
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Adviser: Serge J. Belongie.
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Thesis (Ph.D.)--Cornell University, 2018.
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Includes bibliographical references
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How might we teach machine learning systems about what wine tastes like, or how to appreciate the similarities in different kinds of artwork?
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On its face, this question seems absurd because these notions of similarity are impossible to characterize in meaningful ways. Our work explores what happens when we can embrace this ambiguity. We use new kinds of semi-supervision to learn abstract, intuitive notions of perceptual similarity when labels or dense similarity measures are not available.
520
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Before we can learn about perceptual similarity, we must first show how to capture intuitive notions of similarity from humans in an efficient and principled way that makes as few assumptions as possible about the data structure. Then, we outline ways to combine expensive human expertise with dense machine kernels to ease the human annotation burden. Finally, we will discuss our work on creating a large-scale dataset of artwork that the research community can use to explore these ideas.
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Electronic reproduction.
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Ann Arbor, Mich. :
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ProQuest,
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2018
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Mode of access: World Wide Web
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Computer science.
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79-10B(E).
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10817686
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click for full text (PQDT)
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