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Novel Image Representations and Lear...
~
Venkatesan, Ragav.
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Novel Image Representations and Learning Tasks.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Novel Image Representations and Learning Tasks./
Author:
Venkatesan, Ragav.
Published:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:
138 p.
Notes:
Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:
Dissertation Abstracts International79-03B(E).
Subject:
Computer science. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10642755
ISBN:
9780355505559
Novel Image Representations and Learning Tasks.
Venkatesan, Ragav.
Novel Image Representations and Learning Tasks.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 138 p.
Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Thesis (Ph.D.)--Arizona State University, 2017.
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.
ISBN: 9780355505559Subjects--Topical Terms:
523869
Computer science.
Novel Image Representations and Learning Tasks.
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Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
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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.
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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.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10642755
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