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arXiv · 1609.03666

A Greedy Algorithm to Cluster Specialists

Sébastien Arnold
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Abstract

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.

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Sébastien Arnold. 2016-09-13. A Greedy Algorithm to Cluster Specialists. https://arxiv.org/abs/1609.03666

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