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Publication type: Conference paper
Type of review: Peer review (publication)
Title: Learning neural models for end-to-end clustering
Authors: Meier, Benjamin Bruno
Elezi, Ismail
Amirian, Mohammadreza
Dürr, Oliver
Stadelmann, Thilo
DOI: 10.21256/zhaw-3850
Proceedings: Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR)
Conference details: 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018
Issue Date: 2018
Publisher / Ed. Institution: IAPR
Language: English
Subjects: Perceptual grouping; Learning to cluster; Speech & image clustering
Subject (DDC): 006: Special computer methods
Abstract: 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 <= 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.
Fulltext version: Accepted version
License (according to publishing contract): Not specified
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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