Title: Analysis of classifiers robust to noisy labels Show Chinese title

Title: 对噪声标签鲁棒的分类器分析

Abstract: We explore contemporary robust classification algorithms for overcoming class-dependant labelling noise: Forward, Importance Re-weighting and T-revision. The classifiers are trained and evaluated on class-conditional random label noise data while the final test data is clean. We demonstrate methods for estimating the transition matrix in order to obtain better classifier performance when working with noisy data. We apply deep learning to three data-sets and derive an end-to-end analysis with unknown noise on the CIFAR data-set from scratch. The effectiveness and robustness of the classifiers are analysed, and we compare and contrast the results of each experiment are using top-1 accuracy as our criterion. Show Chinese abstract

Abstract: 我们探索当代稳健分类算法以克服类相关标注噪声：Forward、重要性重加权和T修订。 分类器在类条件随机标签噪声数据上进行训练和评估，而最终测试数据是干净的。 我们展示了估计转换矩阵的方法，以便在处理噪声数据时获得更好的分类器性能。 我们将深度学习应用于三个数据集，并从头开始对CIFAR数据集上的未知噪声进行端到端分析。 分析了分类器的有效性和鲁棒性，并使用top-1准确率作为我们的标准来比较和对比每个实验的结果。