  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.
