TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Meier, Benjamin Bruno
A1  - Elezi, Ismail
A1  - Amirian, Mohammadreza
A1  - Dürr, Oliver
A1  - Stadelmann, Thilo
T1  - Learning neural models for end-to-end clustering
T2  - 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), 19-21 September 2018, Siena, Italy
N2  - 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.
KW  - Perceptual grouping
KW  - Learning to cluster
KW  - Speech & image clustering
Y1  - 2018
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:kon4-opus4-22863
SN  - 978-3-319-99977-7
SB  - 978-3-319-99977-7
SN  - 978-3-319-99978-4
SB  - 978-3-319-99978-4
U6  - https://doi.org/10.1007/978-3-319-99978-4_10
DO  - https://doi.org/10.1007/978-3-319-99978-4_10
SP  - 126
EP  - 138
PB  - Springer
CY  - Cham
ET  - Akzeptierte Version
ER  -