Single and Ensemble CNN Models with Out-Category Penalty in Cifar 10

Abstract

In recent years, CNN have been used in many image recognition tasks. However, most of these CNN models learn only the features of the image, and do not learn the meta-information of the image. In this study, we proposed CNN models that can learn not only image features but also meta-information such as animals and vehicles by imposing an out-category penalty on the cifar10 dataset. As a result, our proposed model was found to be able to learn with meta-information and produce higher accuracy than existing CNN models.