The label noise transition matrix, characterizing the probabilities of a
training instance being wrongly annotated, is crucial to designing popular
solutions to learning with noisy labels. Existing works heavily rely on finding
"anchor points" or their approximates, defined as instances belonging to a
particular class almost surely. Nonetheless, finding anchor points remains a
non-trivial task, and the estimation accuracy is also often throttled by the
number of available anchor points. In this paper, we propose an alternative
option to the above task. Our main contribution is the discovery of an
efficient estimation procedure based on a clusterability condition. We prove
that with clusterable representations of features, using up to third-order
consensuses of noisy labels among neighbor representations is sufficient to
estimate a unique transition matrix. Compared with methods using anchor points,
our approach uses substantially more instances and benefits from a much better
sample complexity. We demonstrate the estimation accuracy and advantages of our
estimates using both synthetic noisy labels (on CIFAR-10/100) and real
human-level noisy labels (on Clothing1M and our self-collected human-annotated
CIFAR-10). Our code and human-level noisy CIFAR-10 labels are available at
this https URL

本文提出了一种基于可簇性条件的有效估计过程，使用邻域表示中的三阶共识即可准确估计唯一的转移矩阵，并证明了相比于使用锚点的方法，我们的方法使用的实例更多，具有更好的采样复杂度。