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arXiv · 1806.11078

A probabilistic constrained clustering for transfer learning and image category discovery

Yen-Chang Hsu·Zhaoyang Lv·Joel Schlosser·Phillip Odom·Zsolt Kira
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Abstract

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 clustering formulation to address scalability issues of previous work in terms of optimizing deeper networks and larger amounts of categories. The proposed objective directly minimizes the negative log-likelihood of cluster assignment with respect to the pairwise constraints, has no hyper-parameters, and demonstrates improved scalability and performance on both supervised learning and unsupervised transfer learning.

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Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira. 2018-06-28. A probabilistic constrained clustering for transfer learning and image category discovery. https://arxiv.org/abs/1806.11078

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