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Deep Learning (DL) has been proven as a powerful recognition method as evidenced by its success in recent computer vision competitions. The most accurate results have been obtained by ensembles of DL models that pool their results. However, such ensembles are computationally costly, making them inapplicable to real-time applications. In this paper, we apply model compression techniques to the problem...