The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

本文提出了一种与多个标注者的混淆有关的置信模型学习方法, 通过对分类器网络添加熵或基于信息的正则化器来鲁棒地估计给定仅含有噪声标签的预测, 并在MNIST、CIFAR-10和FMNIST等数据集上进行了实验, 实验结果表明该方法在各项指标上均优于或与其他现有方法表现相当。

追求基础事实：在注释器噪声存在的情况下学习自信模型并估计不确定性