There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks. However, most existing methods still depend on prior assumptions regarding clean samples amidst different sources of noise (\eg, a pre-defined drop rate or a small subset of clean samples). In this paper, we propose a simple yet powerful idea called \textbf{NPN}, which revolutionizes \textbf{N}oisy label learning by integrating \textbf{P}artial label learning (PLL) and \textbf{N}egative learning (NL). Toward this goal, we initially decompose the given label space adaptively into the candidate and complementary labels, thereby establishing the conditions for PLL and NL. We propose two adaptive data-driven paradigms of label disambiguation for PLL: hard disambiguation and soft disambiguation. Furthermore, we generate reliable complementary labels using all non-candidate labels for NL to enhance model robustness through indirect supervision. To maintain label reliability during the later stage of model training, we introduce a consistency regularization term that encourages agreement between the outputs of multiple augmentations. Experiments conducted on both synthetically corrupted and real-world noisy datasets demonstrate the superiority of NPN compared to other state-of-the-art (SOTA) methods. The source code has been made available at {\color{purple}{\url{https://github.com/NUST-Machine-Intelligence-Laboratory/NPN}}}.

翻译：近年来，半监督学习、对比学习和元学习等不同领域在提升噪声标签学习（NLL）任务方法性能方面的有效性受到了广泛关注。然而，大多数现有方法仍依赖于关于不同噪声源中干净样本的先验假设（例如，预定义的丢弃率或少量干净子集）。本文提出一种名为**NPN**的简单而强大的思想，通过集成**部**分标签学习（PLL）和**负**标签学习（NL）彻底革新噪声标签学习。为实现这一目标，我们首先将给定标签空间自适应地分解为候选标签和互补标签，从而建立PLL和NL的条件。我们为PLL提出了两种自适应数据驱动的标签消歧范式：硬消歧和软消歧。此外，我们利用所有非候选标签生成可靠的互补标签用于NL，通过间接监督增强模型鲁棒性。为在模型训练后期保持标签可靠性，我们引入一致性正则化项，鼓励多种数据增强输出的对齐。在合成噪声和真实噪声数据集上的实验表明，NPN相比其他最先进（SOTA）方法具有优越性。源代码已公开于：{\color{purple}{\url{https://github.com/NUST-Machine-Intelligence-Laboratory/NPN}}}。