• Corpus ID: 49526066

A probabilistic constrained clustering for transfer learning and image category discovery

@article{Hsu2018APC,
  title={A probabilistic constrained clustering for transfer learning and image category discovery},
  author={Yen-Chang Hsu and Zhaoyang Lv and Joel Schlosser and Phillip Odom and Zsolt Kira},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.11078}
}
Neural network-based clustering has recently gained popularity, and in particular a constrained clustering formulation has been proposed to perform transfer learning and image category discovery using deep learning. The core idea is to formulate a clustering objective with pairwise constraints that can be used to train a deep clustering network; therefore the cluster assignments and their underlying feature representations are jointly optimized end-to-end. In this work, we provide a novel… 

Tables from this paper

Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data
TLDR
A generic, end-to-end framework to jointly learn a reliable representation and assign clusters to unlabelled data is presented and Winner-Take-All hashing algorithm is employed on the shared representation space to generate pairwise pseudo labels for unlabelling data to better predict cluster assignments.

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