ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning

• URL: http://arxiv.org/abs/2301.12077v2
• Date: Thu, 18 May 2023 02:07:54 GMT
• Title: ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
• Authors: Mingyu Xu, Zheng Lian, Lei Feng, Bin Liu, Jianhua Tao
• Abstract summary: Noisy partial label learning is an important branch of weakly supervised learning. Most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. We propose a novel framework for noisy with theoretical guarantees, called Adjusting Labeling Mechanism (ALIM)'' It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs.
• Score: 46.53885746394252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. However, detection errors are unavoidable. These errors can accumulate during training and continuously affect model optimization. To this end, we propose a novel framework for noisy PLL with theoretical guarantees, called ``Adjusting Label Importance Mechanism (ALIM)''. It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs. ALIM is a plug-in strategy that can be integrated with existing PLL approaches. Experimental results on benchmark datasets demonstrate that our method can achieve state-of-the-art performance on noisy PLL. \textcolor[rgb]{0.93,0.0,0.47}{Our code can be found in Supplementary Material}.

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