Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks. In this work, we propose a theoretically grounded method that can estimate the noise transition matrix and learn a classifier simultaneously, without relying on the error-prone noisy class-posterior estimation. Concretely, inspired by the characteristics of the stochastic label corruption process, we propose total variation regularization, which encourages the predicted probabilities to be more distinguishable from each other. Under mild assumptions, the proposed method yields a consistent estimator of the transition matrix. We show the effectiveness of the proposed method through experiments on benchmark and real-world datasets.
翻译:许多监督不力的分类方法使用噪音转换矩阵来捕捉等级标签腐败。为了根据噪音数据来估计过渡矩阵,现有方法往往需要根据噪音数据来估计吵闹的阶级前科,由于神经网络的过度自信,这些前科可能不可靠。在这项工作中,我们提出了一个基于理论上依据的方法,可以同时估计噪音过渡矩阵并学习分类器,而不必依赖容易出错的吵闹阶级前科估计。具体地说,根据随机标签腐败过程的特征,我们提出了整体变异规范,这鼓励了预测的概率彼此之间更能区分。在轻度假设下,拟议方法得出了过渡矩阵的一致估计值。我们通过基准和现实世界数据集实验,展示了拟议方法的有效性。