论文标题
双t：减少标签 - 噪声学习中过渡矩阵的估计误差
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
论文作者
论文摘要
过渡矩阵表示从干净标签到嘈杂标签的过渡关系，对于在标签 - 噪声学习中构建统计一致的分类器至关重要。估计过渡矩阵的现有方法在很大程度上依赖于估计噪声的后部。但是，由于标签噪声的随机性，噪声类后验的估计误差可能很大，这会导致过渡矩阵的估计很差。因此，在本文中，我们旨在通过利用分裂和诱导范式来解决这个问题。具体而言，我们引入了一个中级类，以避免直接估计嘈杂的阶层后部。通过这个中级类，可以将原始的过渡矩阵分解为两个易于估计的过渡矩阵的乘积。我们称提出的方法为双T估计器。理论分析和经验结果都说明了双T估计器对估计过渡矩阵的有效性，从而导致更好的分类性能。
The transition matrix, denoting the transition relationship from clean labels to noisy labels, is essential to build statistically consistent classifiers in label-noise learning. Existing methods for estimating the transition matrix rely heavily on estimating the noisy class posterior. However, the estimation error for noisy class posterior could be large due to the randomness of label noise, which would lead the transition matrix to be poorly estimated. Therefore, in this paper, we aim to solve this problem by exploiting the divide-and-conquer paradigm. Specifically, we introduce an intermediate class to avoid directly estimating the noisy class posterior. By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices. We term the proposed method the dual-T estimator. Both theoretical analyses and empirical results illustrate the effectiveness of the dual-T estimator for estimating transition matrices, leading to better classification performances.
