不确定性感知的标签噪声矫正算法OA

Label noise introduced issues of uncertainty into the training process of learning algorithms by reducing confidence in the prediction of true classes.To mitigate the impact of label noise,an uncertain-ty-aware label noise correction(ULC)algorithm for robust classification was proposed.Firstly,based on evidence theory and subjective logic theory,uncertainty was estimated from multiple perspectives and the label information of the sample.Secondly,the dataset was finely divided into three subsets.The noise la-bels within these subsets were then corrected using joint prediction.Finally,to optimize the training ob-jectives,each subset was processed using different regularization strategies.Comparative experiments were conducted on four simulated label noise datasets and two containing real label noise.On CIFAR-10 and CIFAR-100 with 40%pairflip-type label noise,the classification accuracy of ULC was increased by 10.58 percentage points and 15.84 percentage points compared to DivideMix,and the corrected label ac-curacy reached 95.48%and 81.32%,respectively.The simulation results showed that the proposed algo-rithm accurately estimated uncertainty,finely improved the accuracy of corrected labels,and enhanced model generalization performance.