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Learning Perceptual Similarity from ...

Wilber, Michael James.

Learning Perceptual Similarity from Crowds and Machines.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Learning Perceptual Similarity from Crowds and Machines./
作者:	Wilber, Michael James.
面頁冊數:	1 online resource (93 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Computer science. -
電子資源:	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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100 1 $a Wilber, Michael James. $3 1190786
245 1 0 $a Learning Perceptual Similarity from Crowds and Machines.
264 0 $c 2018
300 $a 1 online resource (93 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Serge J. Belongie.
502 $a Thesis (Ph.D.)--Cornell University, 2018.
504 $a Includes bibliographical references
520 $a How might we teach machine learning systems about what wine tastes like, or how to appreciate the similarities in different kinds of artwork?
520 $a 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 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Cornell University. $b Computer Science. $3 1179602
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10817686 $z click for full text (PQDT)

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