In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean distribution to noisy distribution (i.e., clean label transition matrix) has been widely exploited to learn a clean label classifier by employing the noisy data. Motivated by that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal distribution to noisy distribution (i.e., Bayes label transition matrix) and learn a Bayes optimal label classifier. Note that given only noisy data, it is ill-posed to estimate either the clean label transition matrix or the Bayes label transition matrix. But favorably, Bayes optimal labels are less uncertain compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the Bayes label transition matrix, i.e., (a) we could theoretically recover a set of Bayes optimal labels under mild conditions; (b) the feasible solution space is much smaller. By exploiting the advantages, we estimate the Bayes label transition matrix by employing a deep neural network in a parameterized way, leading to better generalization and superior classification performance.
翻译:在标签-噪音学习中,估算过渡矩阵是一个热门话题,因为矩阵在建立统计上一致的分类方面发挥着重要作用。传统上,从清洁分配向噪音分配(即清洁标签过渡矩阵)的过渡一直被广泛利用,通过使用噪音数据学习清洁标签分类。在分类者推动下,主要输出Bayes最佳预测标签,在本文件中,我们研究直接模拟从Bayes最佳分配向噪音分配(即Bayes标签过渡矩阵)的过渡,并学习Bayes最佳标签分类师。注意到,考虑到数据噪音,从清洁标签过渡矩阵或Bayes标签过渡矩阵的估算是不妥的。但是,与清洁标签相比,Bayes最佳标签的最佳标签比起来不太不确定,即,Bayes最佳标签的等级后端标签是一流的矢量,而清洁标签则不是。这样可以直接模拟Bayes标签过渡矩阵的最佳分布,即,即,(a)我们从理论上可以回收一组清洁标签过渡模式,在温和的模型中,我们利用一个更好的空间转换方式,利用一个更好的空间模型,利用一个可行的模型,从而利用一个更好的空间升级的模型。