Single and Ensemble CNN Models with Out-Category Penalty for Image Classification

Yuta Suzuki, Daiki Kuyoshi, Satoshi Yamane


In recent years, the technology of machine learning has been developing rapidly. Among them, neural network technology has had a great impact on various fields such as image recog- nition and natural language processing. Among them, CNN or Convolutional Neural Network have been effective in the field of image recognition. However, most of these CNNs learn only the features of the image, and do not learn the meta-information of the image. In this study, we proposed a CNN and its ensemble method that can learn meta-information by using out- category penalties. Experiments were conducted on the CIFAR-10 and CIFAR-100 datasets, and the results show that the proposed method has high accuracy and small out-category error in both single and ensemble models. 


Convolutional Neural Network; Image Recognition; Ensemble Learning

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