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Neural network-based clustering has recently gained popularity, and inparticular a constrained clustering formulation has been proposed to performtransfer learning and image category discovery using deep learning. The coreidea is to formulate a clustering objective with pairwise constraints that canbe used to train a deep clustering network; therefore the cluster assignmentsand their underlying feature representations are jointly optimized end-to-end.In this work, we provide a novel clustering formulation to address scalabilityissues of previous work in terms of optimizing deeper networks and largeramounts of categories. The proposed objective directly minimizes the negativelog-likelihood of cluster assignment with respect to the pairwise constraints,has no hyper-parameters, and demonstrates improved scalability and performanceon both supervised learning and unsupervised transfer learning.

A probabilistic constrained clustering for transfer learning and image category discovery