提出一种高效方法，动态估计图像分类中的真实噪声类型与比例。
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
- 一阶段算法，实时估计每张图的分布内和分布外噪声
- 在合成与真实数据集上均达到顶尖性能
- 适合处理复杂噪声场景的模型训练，尤其对标签质量差的数据有效
带噪声标签的鲁棒训练是图像分类中的关键挑战，可减少对昂贵清洁标签数据集的依赖。现实数据集常同时包含分布内（ID）和分布外（OOD）的实例相关噪声，而现有方法很少能同时应对这一问题，且缺乏全面的基准数据集。此外，尽管当前方法尝试在训练中识别噪声样本，但并未估计ID与OOD噪声率，从而影响筛选效果，且多采用低效的多阶段算法。本文提出自适应估计分布内与分布外标签噪声（AEON）方法，是一种高效的单阶段噪声标签学习框架，能够动态估计实例相关的ID与OOD噪声率，提升对复杂噪声环境的鲁棒性。同时，我们构建了一个反映真实世界噪声场景的新基准。实验表明，AEON在合成与真实数据集上均达到当前最优性能。
原文摘要 · Abstract (English)
Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets often contain a mix of in-distribution (ID) and out-of-distribution (OOD) instance-dependent label noise, a challenge that is rarely addressed simultaneously by existing methods and is further compounded by the lack of comprehensive benchmarking datasets. Furthermore, even though current noisy-label learning approaches attempt to find noisy-label samples during training, these methods do not aim to estimate ID and OOD noise rates to promote their effectiveness in the selection of such noisy-label samples, and they are often represented by inefficient multi-stage learning algorithms. We propose the Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise (AEON) approach to address these research gaps. AEON is an efficient one-stage noisy-label learning methodology that dynamically estimates instance-dependent ID and OOD label noise rates to enhance robustness to complex noise settings. Additionally, we introduce a new benchmark reflecting real-world ID and OOD noise scenarios. Experiments demonstrate that AEON achieves state-of-the-art performance on both synthetic and real-world datasets
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