An Efficient Core-set Selection Algorithm Based on Difference
With the development of deep learning,the scale of datasets is accumulating at an unprecedented speed,the pro-cess of training is inefficiency.It is usually necessary to simplify the original data set while ensuring similar training effect.In view of this,a core-set selection algorithm based on divergence is proposed.The algorithm uses the iterative method to learn in a supervised learning way,and calculates the divergence values of each data through the voting network framework,and then sorts them to select.The core-set selection experiments on CIFAR,Fashion-MNIST and SVHN datasets are carried out.The results show that the pro-posed algorithm can obtain a core-set size of one fifth of the original size,while the accuracy of the training model is only reduced by less than 5%.At the same time,the generalization error of the core dataset is only 0.13,which makes it more universal.
convolutional neural networkcore set selectionsupervised learningactive learning
国家科技期刊平台
NSTL
万方数据
