个人信息Personal Information
副教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:数学科学学院
学科:计算数学
办公地点:大连理工大学数学科学学院505
联系方式:0411-84708351-8205
电子邮箱:yangjiee@dlut.edu.cn
A New Improved Learning Algorithm for Convolutional Neural Networks
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论文类型:期刊论文
发表时间:2020-03-01
发表刊物:PROCESSES
收录刊物:SCIE
卷号:8
期号:3
关键字:convolutional neural networks; loss function; MNIST; CIFAR-10
摘要:The back-propagation (BP) algorithm is usually used to train convolutional neural networks (CNNs) and has made greater progress in image classification. It updates weights with the gradient descent, and the farther the sample is from the target, the greater the contribution of it to the weight change. However, the influence of samples classified correctly but that are close to the classification boundary is diminished. This paper defines the classification confidence as the degree to which a sample belongs to its correct category, and divides samples of each category into dangerous and safe according to a dynamic classification confidence threshold. Then a new learning algorithm is presented to penalize the loss function with danger samples but not all samples to enable CNN to pay more attention to danger samples and to learn effective information more accurately. The experiment results, carried out on the MNIST dataset and three sub-datasets of CIFAR-10, showed that for the MNIST dataset, the accuracy of Non-improve CNN reached 99.246%, while that of PCNN reached 99.3%; for three sub-datasets of CIFAR-10, the accuracies of Non-improve CNN are 96.15%, 88.93%, and 94.92%, respectively, while those of PCNN are 96.44%, 89.37%, and 95.22%, respectively.