Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 1 of 1
Back to Result List

Learning neural models for end-to-end clustering

  • We 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≤kmax, 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Dürr, Thilo Stadelmann
DOI:https://doi.org/10.1007/978-3-319-99978-4
ISBN:978-3-319-99978-4
Parent Title (English):Artificial neural networks in pattern recognition : 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19 - 21, 2018, Proceedings, (Lecture Notes in Artificial Intelligence ; Vol. 11081)
Publisher:Springer
Document Type:Conference Proceeding
Language:English
Year of Publication:2018
Release Date:2020/01/21
First Page:126
Last Page:138
Institutes:Institut für Optische Systeme - IOS
Licence (English):License LogoLizenzbedingungen Springer