Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often rely on manually specified thresholds, prior knowledge of the noise ratio, or a single metric (either learning dynamics or geometric structure), making them unstable in complex data regimes. This paper proposes a self-adaptive data-cleaning framework that integrates local, global, and learning dynamics cues for robust noisy-label detection. Samples are mapped into a unified low-dimensional feature space through a modular feature concatenation paradigm. We provide two instantiations: a 2D metric integrating class-adaptive KNN-based local disagreement with k-means-based global centroid distance, and a 3D multi-metric that additionally incorporates a z-normalized score. Unlike conventional 1D Gaussian Mixture Models applied to a single scalar metric, our framework performs multi-metric clustering on the feature space to adaptively partition samples into clean-dominant and noise-dominant components without requiring manual thresholds or noise priors. Experiments on CIFAR-10, MNIST, and ImageNet-100 with 5% to 40% symmetric label noise show high recall across settings, including near-perfect recall (>=98%) on ImageNet-100 at 40% noise. Subsequent training yields accuracy gains across evaluated settings, especially under severe corruption on ImageNet-100. These findings suggest that multi-metric integration provides a threshold-free, practical, and low-tuning strategy for noisy label detection.

翻译：深度神经网络（DNN）在大规模标注数据集上表现出色，但在实际应用中，标签常因歧义、人为错误或动态环境而受到污染。过参数化的深度神经网络在训练过程中容易记住这些噪声标签，从而降低模型的准确性和泛化能力。现有的数据清洗和样本选择策略通常依赖人工设定的阈值、噪声比的先验知识或单一指标（如学习动态或几何结构），在复杂数据场景下稳定性不足。本文提出一种自适应数据清洗框架，融合局部、全局和学习动态线索以实现鲁棒的噪声标签检测。通过模块化特征拼接范式，将样本映射到统一的低维特征空间。我们提供了两种实例化方案：一种二维度量，结合基于类别自适应KNN的局部不一致性和基于K均值的全局质心距离；另一种三维多度量，额外引入z归一化分数。与将一维高斯混合模型应用于单一标量度量的传统方法不同，我们的框架在特征空间中进行多度量聚类，从而自适应地将样本划分为干净主导和噪声主导两类，无需人工阈值或噪声先验。在CIFAR-10、MNIST和ImageNet-100上，面对5%至40%的对称标签噪声实验表明，该框架在所有设置下均实现高召回率，其中在ImageNet-100上40%噪声下召回率接近完美（≥98%）。后续训练在评估设置中均获得精度提升，尤其在ImageNet-100严重噪声场景下效果显著。这些结果表明，多度量融合为噪声标签检测提供了一种无阈值、实用且低调参的策略。