用强化学习自动修正噪声标签，提升模型训练效果。
Learning to Clean: Reinforcement Learning for Noisy Label Correction
- 将标签修正建模为强化学习问题，设计状态、动作与奖励机制。
- 在多个基准数据集上表现优于现有最优方法，显著提升模型准确率。
- 适合处理标签质量差的现实场景，尤其对弱监督学习有帮助。
学习带噪声标签的问题在机器学习中极具挑战性，若不妥善处理，会严重降低预测模型性能。本文提出一种新框架，将噪声标签修正视为强化学习（RL）问题。所提方法RLNLC定义了包含数据及其标签的完整状态空间、可进行标签修正的动作空间，以及评估修正效果的奖励机制。该方法通过演员-评论家算法学习深度特征表示策略网络，实现标签修正，并迭代应用以改进训练标签，进而提升预测模型训练效果。大量实验表明，RLNLC在多个基准数据集上持续优于现有的最先进噪声标签学习技术。
原文摘要 · Abstract (English)
The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This paper introduces a novel framework that conceptualizes noisy label correction as a reinforcement learning (RL) problem. The proposed approach, Reinforcement Learning for Noisy Label Correction (RLNLC), defines a comprehensive state space representing data and their associated labels, an action space that indicates possible label corrections, and a reward mechanism that evaluates the efficacy of label corrections. RLNLC learns a deep feature representation based policy network to perform label correction through reinforcement learning, utilizing an actor-critic method. The learned policy is subsequently deployed to iteratively correct noisy training labels and facilitate the training of the prediction model. The effectiveness of RLNLC is demonstrated through extensive experiments on multiple benchmark datasets, where it consistently outperforms existing state-of-the-art techniques for learning with noisy labels.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。