Improving the Training of Convolutional Neural Network using Between-class Distance

Jiani Liu, Xiang Zhang, Yonggang Lu


Recently, Convolutional Neural Networks (CNN) have demonstrated state-of-the-art image classification performance. However, in many cases, it is hard to train the network optimally in multi-class classification. One way to alleviate the problem is to make good use of the training data, and more research work needs to be done on how to use the training data in multi-class classification more efficiently. In this paper we propose a method to make the classification more accurate by analyzing the between-class distance of the deep features of the training data. The specific pattern of the between-class distances is used to improve the training process. It is shown that the proposed method can improve the training on both MNIST and EMNIST datasets.


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