TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning

• URL: http://arxiv.org/abs/2502.07721v1
• Date: Tue, 11 Feb 2025 17:33:48 GMT
• Title: TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning
• Authors: Mengyang Li,
• Abstract summary: This paper introduces TMLC-Net, a Transferable Meta-Learner for Correcting Noisy Labels. TMLC-Net learns a general-purpose label correction strategy that can be readily applied across diverse datasets. We show that TMLC-Net consistently outperforms state-of-the-art methods in terms of both accuracy and robustness to label noise.
• Score: 2.094022863940315
• License:
• Abstract: The prevalence of noisy labels in real-world datasets poses a significant impediment to the effective deployment of deep learning models. While meta-learning strategies have emerged as a promising approach for addressing this challenge, existing methods often suffer from limited transferability and task-specific designs. This paper introduces TMLC-Net, a novel Transferable Meta-Learner for Correcting Noisy Labels, designed to overcome these limitations. TMLC-Net learns a general-purpose label correction strategy that can be readily applied across diverse datasets and model architectures without requiring extensive retraining or fine-tuning. Our approach integrates three core components: (1) Normalized Noise Perception, which captures and normalizes training dynamics to handle distribution shifts; (2) Time-Series Encoding, which models the temporal evolution of sample statistics using a recurrent neural network; and (3) Subclass Decoding, which predicts a corrected label distribution based on the learned representations. We conduct extensive experiments on benchmark datasets with various noise types and levels, demonstrating that TMLC-Net consistently outperforms state-of-the-art methods in terms of both accuracy and robustness to label noise. Furthermore, we analyze the transferability of TMLC-Net, showcasing its adaptability to new datasets and noise conditions, and establishing its potential as a broadly applicable solution for robust deep learning in noisy environments.

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