The label noise transition matrix, denoting the transition probabilities from
clean labels to noisy labels, is crucial for designing statistically robust
solutions. Existing estimators for noise transition matrices, e.g., using
either anchor points or clusterability, focus on computer vision tasks that are
relatively easier to obtain high-quality representations. We observe that tasks
with lower-quality features fail to meet the anchor-point or clusterability
condition, due to the coexistence of both uninformative and informative
representations. To handle this issue, we propose a generic and practical
information-theoretic approach to down-weight the less informative parts of the
lower-quality features. This improvement is crucial to identifying and
estimating the label noise transition matrix. The salient technical challenge
is to compute the relevant information-theoretical metrics using only noisy
labels instead of clean ones. We prove that the celebrated $f$-mutual
information measure can often preserve the order when calculated using noisy
labels. We then build our transition matrix estimator using this distilled
version of features. The necessity and effectiveness of the proposed method are
also demonstrated by evaluating the estimation error on a varied set of tabular
data and text classification tasks with lower-quality features. Code is
available at github.com/UCSC-REAL/BeyondImages.

本文提出了一种信息理论方法来降低低质量特征的影响，用于识别和估计标签噪声转移矩阵，从而改进了噪声转移矩阵的估计，作者在多个任务下验证了该方法的必要性和有效性。

图像以外：估计低质量特征任务的标签噪声转移矩阵

Beyond Images: Label Noise Transition Matrix Estimation for Tasks with  Lower-Quality Features

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。