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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.

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Author:Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver DürrORCiDGND, Thilo Stadelmann
Parent Title (English):8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), 19-21 September 2018, Siena, Italy
Place of publication:Cham
Document Type:Conference Proceeding
Year of Publication:2018
Release Date:2020/01/21
Tag:Perceptual grouping; Learning to cluster; Speech & image clustering
Edition:Akzeptierte Version
First Page:126
Last Page:138
Institutes:Institut für Optische Systeme - IOS
DDC functional group:006 Spezielle Computerverfahren
Open Access?:Ja
Licence (German):License LogoUrheberrechtlich geschützt