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dc.contributor.authorShankar, S
dc.contributor.authorRobertson, D
dc.contributor.authorIoannou, Y
dc.contributor.authorCriminisi, A
dc.contributor.authorCipolla, R
dc.date.accessioned2018-03-23T14:51:08Z
dc.date.available2018-03-23T14:51:08Z
dc.date.issued2016-12-12
dc.identifier.isbn9781467388504
dc.identifier.issn1063-6919
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/274299
dc.description.abstract© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks [11, 27]. However, a question of paramount importance is somewhat unanswered in deep learning research - is the selected CNN optimal for the dataset in terms of accuracy and model size? In this paper, we intend to answer this question and introduce a novel strategy that alters the architecture of a given CNN for a specified dataset, to potentially enhance the original accuracy while possibly reducing the model size. We use two operations for architecture refinement, viz. stretching and symmetrical splitting. Stretching increases the number of hidden units (nodes) in a given CNN layer, while a symmetrical split of say K between two layers separates the input and output channels into K equal groups, and connects only the corresponding input-output channel groups. Our procedure starts with a pre-trained CNN for a given dataset, and optimally decides the stretch and split factors across the network to refine the architecture. We empirically demonstrate the necessity of the two operations. We evaluate our approach on two natural scenes attributes datasets, SUN Attributes [16] and CAMIT-NSAD [20], with architectures of GoogleNet and VGG-11, that are quite contrasting in their construction. We justify our choice of datasets, and show that they are interestingly distinct from each other, and together pose a challenge to our architectural refinement algorithm. Our results substantiate the usefulness of the proposed method.
dc.publisherIEEE
dc.titleRefining Architectures of Deep Convolutional Neural Networks
dc.typeConference Object
prism.endingPage2220
prism.publicationDate2016
prism.publicationNameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
prism.startingPage2212
prism.volume2016-December
dc.identifier.doi10.17863/CAM.21424
rioxxterms.versionofrecord10.1109/CVPR.2016.243
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-12-12
dc.contributor.orcidIoannou, Yani [0000-0002-9797-5888]
dc.contributor.orcidCipolla, Roberto [0000-0002-8999-2151]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.conference-name2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
pubs.conference-start-date2016-06-27
pubs.conference-finish-date2016-06-30


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