A new learning algorithm based on strengthening boundary samples for convolutional neural networks

Abstract

CNN is an artificial neural network that can automatically extract features with relatively few parameters, which is the advantage of CNN in image classification tasks. The purpose of this paper is to propose a new algorithm to improve the classification performance of CNN by strengthening boundary samples. The samples with predicted values near the classification boundary are recorded as hard samples. In this algorithm, the errors of hard samples are added as a penalty term of the original loss function. Multi-classification and binary classification experiments were performed using the MNIST data set and three sub-data sets of CIFAR-10, respectively. The experimental results prove that the accuracy of the new algorithm is improved in both binary classification and multi-classification problems.