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Status: Bibliographieeintrag
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Verfasst von:Bautista, Miguel [VerfasserIn]   i
 Sanakoyeu, Artsiom [VerfasserIn]   i
 Ommer, Björn [VerfasserIn]   i
Titel:Deep unsupervised similarity learning using partially ordered sets
Verf.angabe:Miguel A. Bautista, Artsiom Sanakoyeu, Björn Ommer
E-Jahr:2017
Jahr:09 November 2017
Umfang:10 S.
Teil:year:2017
 pages:1923-1932
 extent:10
Fussnoten:Gesehen am 13.02.2018
Titel Quelle:Enthalten in: IEEE Conference on Computer Vision and Pattern Recognition (30. : 2016 : Honolulu, Hawaii)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ort Quelle:Piscataway, NJ : IEEE, 2017
Jahr Quelle:2017
Band/Heft Quelle:(2017), Seite 1923-1932
Abstract:Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.
DOI:doi:10.1109/CVPR.2017.208
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: http://dx.doi.org/10.1109/CVPR.2017.208
 DOI: https://doi.org/10.1109/CVPR.2017.208
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:CNN
 compact surrogate classes
 Computational modeling
 Computer vision
 deep unsupervised similarity learning
 detailed pose estimation
 fine-grained similarities
 grouping procedure
 image classification
 learning (artificial intelligence)
 local partial orders
 neural nets
 object classification
 partial ordering task
 partially ordered sets
 pose estimation
 similarity learning
 training data
 unsupervised approach
 Visualization
K10plus-PPN:156978292X
Verknüpfungen:→ Sammelwerk

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