用元学习迭代剔除噪声数据，提升工业质检中异常检测的鲁棒性。
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
- 通过MAML与四分位距剔除法实现训练数据的自适应清洗
- 在MVTec和KSDD2数据集上噪声环境下准确率显著提升
- 适合工业场景中存在分布外样本或标注噪声的异常检测任务
本研究针对工业质检中异常检测模型在噪声数据下的性能问题，提出利用元学习的适应能力识别并剔除噪声训练样本以优化学习过程。模型结合模型无关元学习（MAML）与基于四分位距的迭代剔除机制，增强对异常模式的辨别能力。在MVTec和KSDD2两个标准数据集上的实验表明，该方法不仅在高噪声环境中表现优异，还能在干净数据集中识别出分布外样本，显著优于传统模型。
原文摘要 · Abstract (English)
This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leverage the adaptation ability of meta learning approaches to identify and reject noisy training data to improve the learning process. In our model, we employ Model Agnostic Meta Learning (MAML) and an iterative refinement process through an Inter-Quartile Range rejection scheme to enhance their adaptability and robustness. This approach significantly improves the models capability to distinguish between normal and defective conditions. Our results of experiments conducted on well known MVTec and KSDD2 datasets demonstrate that the proposed method not only excels in environments with substantial noise but can also contribute in case of a clear training set, isolating those samples that are relatively out of distribution, thus offering significant improvements over traditional models.
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