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Novel Image Representations and Lear...

Arizona State University.

Novel Image Representations and Learning Tasks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Novel Image Representations and Learning Tasks./
Author:	Venkatesan, Ragav.
Description:	1 online resource (138 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355505559

Novel Image Representations and Learning Tasks.
Venkatesan, Ragav.

Novel Image Representations and Learning Tasks. - 1 online resource (138 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Computer Vision as a field has gone through significant changes in the last decade. The field has seen tremendous success in designing learning systems with hand-crafted features and in using representation learning to extract better features. In this dissertation some novel approaches to representation learning and task learning are studied. Multiple-instance learning which is generalization of supervised learning, is one example of task learning that is discussed. In particular, a novel non-parametric k-NN-based multiple-instance learning is proposed, which is shown to outperform other existing approaches. This solution is applied to a diabetic retinopathy pathology detection problem effectively.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355505559Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Novel Image Representations and Learning Tasks.
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035 $a (MiAaPQ)asu:17450
035 $a AAI10642755
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Venkatesan, Ragav. $3 1144619
245 1 0 $a Novel Image Representations and Learning Tasks.
264 0 $c 2017
300 $a 1 online resource (138 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: 79-03(E), Section: B.
500 $a Adviser: Baoxin Li.
502 $a Thesis (Ph.D.) $c Arizona State University $d 2017.
504 $a Includes bibliographical references
520 $a Computer Vision as a field has gone through significant changes in the last decade. The field has seen tremendous success in designing learning systems with hand-crafted features and in using representation learning to extract better features. In this dissertation some novel approaches to representation learning and task learning are studied. Multiple-instance learning which is generalization of supervised learning, is one example of task learning that is discussed. In particular, a novel non-parametric k-NN-based multiple-instance learning is proposed, which is shown to outperform other existing approaches. This solution is applied to a diabetic retinopathy pathology detection problem effectively.
520 $a In cases of representation learning, generality of neural features are investigated first. This investigation leads to some critical understanding and results in feature generality among datasets. The possibility of learning from a mentor network instead of from labels is then investigated. Distillation of dark knowledge is used to efficiently mentor a small network from a pre-trained large mentor network. These studies help in understanding representation learning with smaller and compressed networks.
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
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Arizona State University. $b Computer Science. $3 845377
773 0 $t Dissertation Abstracts International $g 79-03B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10642755 $z click for full text (PQDT)

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