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.

利用不可靠偏标签强健对比学习方法增强模型对不可靠偏标签的鲁棒性，并结合基于 KNN 的候选标签集校正以及基于一致性正则化的标签消歧来提高标签质量和增强 URRL 框架中的表示学习能力。实验证明该方法在不同数据集上的性能超过了现有方法，并从期望最大化算法的角度对此方法进行了理论分析。

不可靠局部标签学习的鲁棒表示学习

Robust Representation Learning for Unreliable Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning problem
in which each instance is associated with a candidate label set, and among
which only one is true. However, the assumption that the ground-truth label is
always among the candidate label set would be unrealistic, as the reliability
of the candidate label sets in real-world applications cannot be guaranteed by
annotators. Therefore, a generalized PLL named Unreliable Partial Label
Learning (UPLL) is proposed, in which the true label may not be in the
candidate label set. Due to the challenges posed by unreliable labeling,
previous PLL methods will experience a marked decline in performance when
applied to UPLL. To address the issue, we propose a two-stage framework named
Unreliable Partial Label Learning with Recursive Separation (UPLLRS). In the
first stage, the self-adaptive recursive separation strategy is proposed to
separate the training set into a reliable subset and an unreliable subset. In
the second stage, a disambiguation strategy is employed to progressively
identify the ground-truth labels in the reliable subset. Simultaneously,
semi-supervised learning methods are adopted to extract valuable information
from the unreliable subset. Our method demonstrates state-of-the-art
performance as evidenced by experimental results, particularly in situations of
high unreliability.

针对部分标签学习中标签可能不可靠的情况，提出了一种名为 UPLLRS 的两阶段框架，其中自适应递归分离策略用于训练数据分成可靠和不可靠子集，消除不可靠的标签后在可靠子集上进行半监督学习。该方法在实验结果方面表现出最先进的性能，特别适用于高度不可靠的情况。