@inproceedings{MeierEleziAmirianetal.2018,
  author    = {Meier, Benjamin Bruno and Elezi, Ismail and Amirian, Mohammadreza and D{\"u}rr, Oliver and Stadelmann, Thilo},
  title     = {Learning neural models for end-to-end clustering},
  booktitle = {8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), 19-21 September 2018, Siena, Italy},
  edition   = {Akzeptierte Version},
  isbn      = {978-3-319-99977-7},
  doi       = {10.1007/978-3-319-99978-4_10},
  institution = {Institut f{\"u}r Optische Systeme - IOS},
  pages     = {126 -- 138},
  year      = {2018},
  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≤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.},
  language  = {en}
}