Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. However, this idealistic assumption may not always hold due to potential annotation inaccuracies, meaning the ground-truth may not be present in the candidate label set. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels, often resulting in a sub-optimal performance with existing methods. To address this challenge, we propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively. Concurrently, we propose a dual strategy that combines KNN-based candidate label set correction and consistency-regularization-based label disambiguation to refine label quality and enhance the ability of representation learning within the URRL framework. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art PLL methods on various datasets with diverse degrees of unreliability and ambiguity. Furthermore, we provide a theoretical analysis of our approach from the perspective of the expectation maximization (EM) algorithm. Upon acceptance, we pledge to make the code publicly accessible.

翻译：部分标签学习（PLL）是一种弱监督学习形式，其中每个训练实例被分配一组候选标签，但仅有一个标签为真实标签。然而，由于潜在的标注不准确性，这一理想化假设可能不总是成立，意味着真实标签可能不在候选标签集中。这被称为不可靠部分标签学习（UPLL），其因部分标签固有的不可靠性和模糊性而引入额外复杂性，常导致现有方法性能欠佳。为应对这一挑战，我们提出了不可靠性鲁棒表示学习框架（URRL），该框架利用不可靠性鲁棒的对比学习有效增强模型对不可靠部分标签的抵御能力。同时，我们提出一种结合基于KNN的候选标签集校正与基于一致性正则化的标签消歧的双重策略，以在URRL框架内优化标签质量并提升表示学习能力。大量实验表明，所提方法在具有不同不可靠性和模糊性程度的各种数据集上均优于最先进的PLL方法。此外，我们从期望最大化（EM）算法的角度对所提方法进行了理论分析。论文被接收后，我们承诺将公开代码。