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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3850
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dc.contributor.authorMeier, Benjamin Bruno-
dc.contributor.authorElezi, Ismail-
dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorDürr, Oliver-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2018-07-09T12:53:08Z-
dc.date.available2018-07-09T12:53:08Z-
dc.date.issued2018-
dc.identifier.isbn978-3-319-99977-7de_CH
dc.identifier.isbn978-3-319-99978-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7727-
dc.description.abstractWe propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters k, and for each 1 <= k <= k_max, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this “learning to cluster” and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsNot specifiedde_CH
dc.subjectPerceptual groupingde_CH
dc.subjectLearning to clusterde_CH
dc.subjectSpeech & image clusteringde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleLearning neural models for end-to-end clusteringde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1007/978-3-319-99978-4_10de_CH
dc.identifier.doi10.21256/zhaw-3850-
zhaw.conference.details8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end138de_CH
zhaw.pages.start126de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.series.number11081de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
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Meier, B. B., Elezi, I., Amirian, M., Dürr, O., & Stadelmann, T. (2018). Learning neural models for end-to-end clustering [Conference paper]. Artificial Neural Networks in Pattern Recognition, 126–138. https://doi.org/10.1007/978-3-319-99978-4_10
Meier, B.B. et al. (2018) ‘Learning neural models for end-to-end clustering’, in Artificial Neural Networks in Pattern Recognition. Springer, pp. 126–138. Available at: https://doi.org/10.1007/978-3-319-99978-4_10.
B. B. Meier, I. Elezi, M. Amirian, O. Dürr, and T. Stadelmann, “Learning neural models for end-to-end clustering,” in Artificial Neural Networks in Pattern Recognition, 2018, pp. 126–138. doi: 10.1007/978-3-319-99978-4_10.
MEIER, Benjamin Bruno, Ismail ELEZI, Mohammadreza AMIRIAN, Oliver DÜRR und Thilo STADELMANN, 2018. Learning neural models for end-to-end clustering. In: Artificial Neural Networks in Pattern Recognition. Conference paper. Springer. 2018. S. 126–138. ISBN 978-3-319-99977-7
Meier, Benjamin Bruno, Ismail Elezi, Mohammadreza Amirian, Oliver Dürr, and Thilo Stadelmann. 2018. “Learning Neural Models for End-to-End Clustering.” Conference paper. In Artificial Neural Networks in Pattern Recognition, 126–38. Springer. https://doi.org/10.1007/978-3-319-99978-4_10.
Meier, Benjamin Bruno, et al. “Learning Neural Models for End-to-End Clustering.” Artificial Neural Networks in Pattern Recognition, Springer, 2018, pp. 126–38, https://doi.org/10.1007/978-3-319-99978-4_10.


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