Complementary-label learning is a weakly supervised learning problem in which
each training example is associated with one or multiple complementary labels
indicating the classes to which it does not belong. Existing consistent
approaches have relied on the uniform distribution assumption to model the
generation of complementary labels, or on an ordinary-label training set to
estimate the transition matrix. However, both conditions may not be satisfied
in real-world scenarios. In this paper, we propose a novel complementary-label
learning approach that does not rely on these conditions. We find that
complementary-label learning can be expressed as a set of negative-unlabeled
binary classification problems when using the one-versus-rest strategy. This
observation allows us to propose a risk-consistent approach with theoretical
guarantees. Furthermore, we introduce a risk correction approach to address
overfitting problems when using complex models. We also prove the statistical
consistency and convergence rate of the corrected risk estimator. Extensive
experimental results on both synthetic and real-world benchmark datasets
validate the superiority of our proposed approach over state-of-the-art
methods.

本文提出了一种新颖的互补标签学习方法，不依赖于均匀分布假设或普通标签训练集，将互补标签学习表达为一组负无标签二元分类问题，并采用风险一致性方法和风险修正方法解决过拟合问题，实验证明我们的方法优于现有方法。

学习补充标签的重访：一种通过负无标签学习的一致方法

Learning with Complementary Labels Revisited: A Consistent Approach via  Negative-Unlabeled Learning

Recently, learning with soft labels has been shown to achieve better
performance than learning with hard labels in terms of model generalization,
calibration, and robustness. However, collecting pointwise labeling confidence
for all training examples can be challenging and time-consuming in real-world
scenarios. This paper delves into a novel weakly supervised binary
classification problem called confidence-difference (ConfDiff) classification.
Instead of pointwise labeling confidence, we are given only unlabeled data
pairs with confidence difference that specifies the difference in the
probabilities of being positive. We propose a risk-consistent approach to
tackle this problem and show that the estimation error bound achieves the
optimal convergence rate. We also introduce a risk correction approach to
mitigate overfitting problems, whose consistency and convergence rate are also
proven. Extensive experiments on benchmark data sets and a real-world
recommender system data set validate the effectiveness of our proposed
approaches in exploiting the supervision information of the confidence
difference.

使用软标签进行学习在模型泛化、校准和鲁棒性方面表现更好。本文提出了一种称为置信度差异分类的弱监督二分类问题，并提出了一种风险一致的方法来解决该问题。通过大量的实验证明了该方法在利用置信度差异的监督信息方面的有效性。