Despite the large progress in supervised learning with Neural Networks, there are significant challenges in obtaining high-quality, large-scale and accurately labeled datasets. In this context, in this paper we address the problem of classification in the presence of label noise and more specifically, both close-set and open-set label noise, that is when the true label of a sample may, or may not belong to the set of the given labels. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space; a relabeling mechanism that relies on the confidence of the classifier across subsequent iterations; and a training strategy that trains the encoder both with a self-consistency loss and the classifier-encoder with the cross-entropy loss on the selected samples alone. Without bells and whistles, such as co-training so as to reduce the self-confirmation bias, and with robustness with respect to settings of its few hyper-parameters, our method significantly surpasses previous methods on both CIFAR10/CIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.

本文提出了一个基于KNN分类和参数化模型分类相结合的方法，命名为Sample Selection and Relabelling（SSR），用于解决存在未知噪声数据的学习问题（Learning with Unknown Label Noise），该方法在CIFAR10 / CIFAR100以及WebVision，Clothing1M和ANIMAL-10N等真实世界噪声数据集上比以前的方法表现更好。

SSR:学习未知标签噪声的高效稳健框架