For learning with noisy labels, the transition matrix, which explicitly
models the relation between noisy label distribution and clean label
distribution, has been utilized to achieve the statistical consistency of
either the classifier or the risk. Previous researches have focused more on how
to estimate this transition matrix well, rather than how to utilize it. We
propose good utilization of the transition matrix is crucial and suggest a new
utilization method based on resampling, coined RENT. Specifically, we first
demonstrate current utilizations can have potential limitations for
implementation. As an extension to Reweighting, we suggest the Dirichlet
distribution-based per-sample Weight Sampling (DWS) framework, and compare
reweighting and resampling under DWS framework. With the analyses from DWS, we
propose RENT, a REsampling method with Noise Transition matrix. Empirically,
RENT consistently outperforms existing transition matrix utilization methods,
which includes reweighting, on various benchmark datasets. Our code is
available at https://github.com/BaeHeeSun/RENT.

利用噪声标签的过渡矩阵来实现分类器或风险的统计一致性已经成为先前研究的焦点，而我们提出了一种新的利用方法，基于重新采样的 RENT，并通过 DWS 框架对其与重新加权方法进行了比较。我们的实验证明，RENT 在各种基准数据集上始终优于其他过渡矩阵利用方法，包括重新加权。