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Probabilistic Siamese network for le...

Liu, Chen.

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Probabilistic Siamese network for learning representations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic Siamese network for learning representations./
作者:	Liu, Chen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2013,
面頁冊數:	54 p.
附註:	Source: Masters Abstracts International, Volume: 76-06.
Contained By:	Masters Abstracts International76-06.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1571140
ISBN:	9781321408324

Probabilistic Siamese network for learning representations.
Liu, Chen.

Probabilistic Siamese network for learning representations. - Ann Arbor : ProQuest Dissertations & Theses, 2013 - 54 p.

Source: Masters Abstracts International, Volume: 76-06.

Thesis (M.A.Sc.)--University of Toronto (Canada), 2013.

This item must not be sold to any third party vendors.

We explore the training of deep neural networks to produce vector representations using weakly labelled information in the form of binary similarity labels for pairs of training images. Previous methods such as siamese networks, IMAX and others, have used fixed cost functions such as L 1, L2-norms and mutual information to drive the representations of similar images together and different images apart. In this work, we formulate learning as maximizing the likelihood of binary similarity labels for pairs of input images, under a parameterized probabilistic similarity model. We describe and evaluate several forms of the similarity model that account for false positives and false negatives differently. We extract representations of MNIST, AT&T ORL and COIL-100 images and use them to obtain classification results. We compare these results with state-of-the-art techniques such as deep neural networks and convolutional neural networks. We also study our method from a dimensionality reduction prospective.

ISBN: 9781321408324Subjects--Topical Terms:

649834
Electrical engineering.

Probabilistic Siamese network for learning representations.
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520 $a We explore the training of deep neural networks to produce vector representations using weakly labelled information in the form of binary similarity labels for pairs of training images. Previous methods such as siamese networks, IMAX and others, have used fixed cost functions such as L 1, L2-norms and mutual information to drive the representations of similar images together and different images apart. In this work, we formulate learning as maximizing the likelihood of binary similarity labels for pairs of input images, under a parameterized probabilistic similarity model. We describe and evaluate several forms of the similarity model that account for false positives and false negatives differently. We extract representations of MNIST, AT&T ORL and COIL-100 images and use them to obtain classification results. We compare these results with state-of-the-art techniques such as deep neural networks and convolutional neural networks. We also study our method from a dimensionality reduction prospective.
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