Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale pre-training tasks is often impractical. Nonetheless, existing noise mitigation methods often encounter limitations in practical applications due to their task-specific design, model dependency, and significant computational overhead. In this work, we exploit the properties of high-dimensional orthogonality to identify a robust and effective boundary in cone space for separating clean and noisy samples. Building on this, we propose One-step Anti-Noise (OSA), a model-agnostic noisy label mitigation paradigm that employs an estimator model and a scoring function to assess the noise level of input pairs through just one-step inference, a cost-efficient process. We empirically demonstrate the superiority of OSA, highlighting its enhanced training robustness, improved task transferability, ease of deployment, and reduced computational costs across various benchmarks, models, and tasks. Our code is released at https://github.com/leolee99/OSA.

本研究针对噪声标签对训练过程的不良影响，提出了一种新的减缓方法，即“一步式抗噪声”（OSA），通过高维正交性的特性有效区分干净样本和噪声样本。OSA在单一步推理中评估输入对的噪声水平，展示了在多种基准、模型和任务中，显著增强的训练鲁棒性和降低的计算成本。 

一步式噪声标签减缓