Partial Label Learning (PLL) aims to train a classifier when each training
instance is associated with a set of candidate labels, among which only one is
correct but is not accessible during the training phase. The common strategy
dealing with such ambiguous labeling information is to disambiguate the
candidate label sets. Nonetheless, existing methods ignore the disambiguation
difficulty of instances and adopt the single-trend training mechanism. The
former would lead to the vulnerability of models to the false positive labels
and the latter may arouse error accumulation problem. To remedy these two
drawbacks, this paper proposes a novel approach termed "Network Cooperation
with Progressive Disambiguation" (NCPD) for PLL. Specifically, we devise a
progressive disambiguation strategy of which the disambiguation operations are
performed on simple instances firstly and then gradually on more complicated
ones. Therefore, the negative impacts brought by the false positive labels of
complicated instances can be effectively mitigated as the disambiguation
ability of the model has been strengthened via learning from the simple
instances. Moreover, by employing artificial neural networks as the backbone,
we utilize a network cooperation mechanism which trains two networks
collaboratively by letting them interact with each other. As two networks have
different disambiguation ability, such interaction is beneficial for both
networks to reduce their respective disambiguation errors, and thus is much
better than the existing algorithms with single-trend training process.
Extensive experimental results on various benchmark and practical datasets
demonstrate the superiority of our NCPD to other state-of-the-art PLL methods.

本文提出了一种名为网络协作与逐层消歧 (NCPD) 的新方法，针对存在歧义标注信息的部分标签学习问题进行模型训练，其中采用了逐步消歧策略，弱化了复杂实例带来的误差累积问题，并利用协作的人工神经网络实现更好的性能。实验结果表明，该方法优于现有的同类算法。

网络协作逐步消除歧义的局部标签学习

Network Cooperation with Progressive Disambiguation for Partial Label  Learning

Partial label learning deals with the problem where each training instance is
assigned a set of candidate labels, only one of which is correct. This paper
provides the first attempt to leverage the idea of self-training for dealing
with partially labeled examples. Specifically, we propose a unified formulation
with proper constraints to train the desired model and perform pseudo-labeling
jointly. For pseudo-labeling, unlike traditional self-training that manually
differentiates the ground-truth label with enough high confidence, we introduce
the maximum infinity norm regularization on the modeling outputs to
automatically achieve this consideratum, which results in a convex-concave
optimization problem. We show that optimizing this convex-concave problem is
equivalent to solving a set of quadratic programming (QP) problems. By
proposing an upper-bound surrogate objective function, we turn to solving only
one QP problem for improving the optimization efficiency. Extensive experiments
on synthesized and real-world datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art partial label learning
approaches.

本文提出了一种新的方法，采用自我训练的方式来处理部分标签数据，其中采用了基于最大无穷范数的伪标签技术来自动实现基准标签的分类，并通过使用简单的二次规划方法进行拟合优化。通过在自制和真实世界数据集上的测试，证明了所提出的方法比现有的部分标签学习方法更加有效。