智能系统学报2024，Vol.19Issue(2)：267-277,11.DOI:10.11992/tis.202303014
基于K-means聚类和特征空间增强的噪声标签深度学习算法
A noisy label deep learning algorithm based on K-means clustering and feature space augmentation
摘要
Abstract
The performance of neural networks in deep learning relies on high-quality samples.However,the presence of noisy labels reduces the classification accuracy of the network.To reduce the impact of noisy labels,we propose a learning algorithm that categorizes training samples into clean and noisy subsets,assigning pseudo-labels to the noisy samples using a semisupervised learning algorithm.Despite these measures,the performance of the noisy label learning algorithm can be hindered by inaccurate pseudo-labels and a lack of sufficient training samples.To address the afore-mentioned problems,we propose a noisy label deep learning algorithm that leverages K-means clustering and feature space augmentation.First,the algorithm applies the K-means clustering algorithm to cluster the clean samples based on their labels.It then selects noisy samples that are difficult to classify according to the distance between the noisy samples and the cluster center.This process enhances the quality of the training samples.Second,the mix-up algorithm is used to expand both the clean and noisy samples,thereby increasing the number of training samples.Finally,a feature space augmentation algorithm is used to suppress the noise samples generated by the mix-up algorithm,leading to im-proved network classification accuracy.The effectiveness of the proposed algorithm has been validated on four data sets:CIFAR10,CIFAR100,MNIST,and ANIMAL-10.关键词
噪声标签学习/深度学习/半监督学习/机器学习/神经网络/K-means聚类/特征空间增强/mixup算法Key words
noisy label learning/deep learning/semisupervised learning/machine learning/neural network/K-means clustering/feature space augmentation/mix-up algorithm分类
信息技术与安全科学引用本文复制引用
吕佳,邱小龙..基于K-means聚类和特征空间增强的噪声标签深度学习算法[J].智能系统学报,2024,19(2):267-277,11.基金项目
国家自然科学基金重大项目(11991024) （11991024）
重庆市教委"成渝地区双城经济圈建设"科技创新项目(KJCX2020024) （KJCX2020024）
重庆市高校创新研究群体资助项目(CXQT20015) （CXQT20015）
重庆市教委科研项目重点项目(KJZD-K202200511). （KJZD-K202200511）
