Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning

• URL: http://arxiv.org/abs/2312.09505v1
• Date: Fri, 15 Dec 2023 03:06:19 GMT
• Title: Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
• Authors: Mengmeng Sheng, Zeren Sun, Zhenhuang Cai, Tao Chen, Yichao Zhou, Yazhou Yao
• Abstract summary: We propose a simple yet powerful idea called textbfNPN, which revolutionizes textbfNoisy label learning. We generate reliable complementary labels using all non-candidate labels for NL to enhance model robustness through indirect supervision. 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.
• Score: 23.847160480176697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}}}.

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