Learning a better representation with neural networks is a challenging problem, which has been tackled from different perspectives in the past few years. In this work, we focus on learning a representation that would be useful in a clustering task. We introduce two novel loss components that substantially improve the quality of produced clusters, are simple to apply to arbitrary models and cost functions, and do not require a complicated training procedure. We perform an extensive set of experiments, supervised and unsupervised, and evaluate the proposed loss components on two most common types of models, Recurrent Neural Networks and Convolutional Neural Networks, showing that the approach we propose consistently improves the quality of KMeans clustering in terms of mutual information scores and outperforms previously proposed methods.
A novel loss component that forces the network to learn a representation that is well-suited for clustering during training for a classification task. This paper proposes two regularization terms based on a compound hinge loss over the KL divergence between two softmax-normalized input arguments to encourage learning disentangled representations Proposal for two regularizers intended to make the representations learned in the penultimate layer of a classifier more conforming to inherent structure in the data.