面向实例依赖标签噪声学习的动态混合噪声识别方法OA

In learning with instance-dependent label noise(IDN),semi-supervised methods could mitigate noise interference and leverage feature information,but their effectiveness depended on accurate noise identification and was susceptible to the choice of recognition technique.To address this limitation,a robust feature-centroid mecha-nism was designed to weaken the influence of unreliable samples and a distribution-adaptive dynamic mixture model(DMM)was proposed based on feature similarity.Pairwise feature similarities was extracted,both Gaussian Mix-ture Models(GMM)and Beta Mixture Models(BMM)were used to fit these similarity distributions,and dynami-cally to fuse their outputs to achieve more accurate noise identification.A semi-supervised learning strategy was then integrated to complete the training process.On artificially corrupted CIFAR-10 and CIFAR-100 datasets,our method achieved state-of-the-art performance.On real-world noisy benchmarks Animal-10N and Clothing1M,it at-tained classification accuracies of 84.21%and 75.80%,respectively,outperforming representative existing approa-ches and demonstrating the effectiveness and applicability of our approach for IDN learning tasks.