Partial-label learning (PLL) is a multi-class classification problem, where
each training example is associated with a set of candidate labels. Even though
many practical PLL methods have been proposed in the last two decades, there
lacks a theoretical understanding of the consistency of those methods-none of
the PLL methods hitherto possesses a generation process of candidate label
sets, and then it is still unclear why such a method works on a specific
dataset and when it may fail given a different dataset. In this paper, we
propose the first generation model of candidate label sets, and develop two
novel PLL methods that are guaranteed to be provably consistent, i.e., one is
risk-consistent and the other is classifier-consistent. Our methods are
advantageous, since they are compatible with any deep network or stochastic
optimizer. Furthermore, thanks to the generation model, we would be able to
answer the two questions above by testing if the generation model matches given
candidate label sets. Experiments on benchmark and real-world datasets validate
the effectiveness of the proposed generation model and two PLL methods.

本文提出了候选标签集的生成模型，开发了两种崭新且可证明一致的 PLL 方法，这些方法优越的原因是它们与任何深度网络或随机优化器兼容，并且通过测试生成模型是否匹配给定候选标签集，能够回答 PLL 方法工作的数据集和失败时的原因等问题。

可证明一致的部分标签学习

Provably Consistent Partial-Label Learning

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。