Convolutional neural network classifier incorporating misclassification information

In this paper we introduce misclassification information for the improved training of convolutional neural network classifiers (CNNCs) for image recognition. We construct a additional autoencoder neural network, called tutor, that forces the CNNCs to learn the difference between the misclassified picture and the picture corresponding to the misclassified category. Making full use of the classification results to guide the CNNCs for purposeful learning is expected to improve the learning efficiency and classification performance. We integrate the proposed tutor into several state-of-the-art CNNCs architectures and demonstrate improvement in their recognition performance on CIFAR-10/100 and MNIST datasets. Our results suggest that making the most of misclassification information to guide the training of the model can lead to significant performance improvement.