  Several recent deep neural networks experiments leverage the
generalist-specialist paradigm for classification. However, no formal study
compared the performance of different clustering algorithms for class
assignment. In this paper we perform such a study, suggest slight modifications
to the clustering procedures, and propose a novel algorithm designed to
optimize the performance of of the specialist-generalist classification system.
Our experiments on the CIFAR-10 and CIFAR-100 datasets allow us to investigate
situations for varying number of classes on similar data. We find that our
\emph{greedy pairs} clustering algorithm consistently outperforms other
alternatives, while the choice of the confusion matrix has little impact on the
final performance.
