An Efficient Core-set Selection Algorithm Based on Difference
WANG Zongchi
LIU Jian
WANG Pei
ZHAO Xingbo
YU Jiageng
TAO Qingchuan
Abstract：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.
Keywords：convolutional neural networkcore set selectionsupervised learningactive learning
Publication Date：2024-05-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1304-1309,1316 )