记忆蒸馏选择用于噪声鲁棒异常检测
Memory-Distilled Selection for Noise-Robust Anomaly Detection
AI总结：
提出基于数据选择的训练算法MeDS，通过随机子采样构建部分记忆集成，利用稀疏性作为低通滤波器捕获名义模式，再蒸馏为重建分数网络，实现噪声鲁棒的异常检测。
摘要：
数据污染下的异常检测对于在工业环境中部署无监督缺陷检测至关重要，因为整理完全干净的训练集是不切实际的。然而，现有方法对污染敏感，随着噪声比例增加，性能显著下降。在本文中，我们提出记忆蒸馏选择（MeDS），一种基于数据选择的训练算法。MeDS通过随机子采样构建部分记忆集成，其中产生的稀疏性作为低通滤波器，在广泛的噪声比例下捕获名义模式，从而实现对污染样本的粗粒度识别。然后，将到自举记忆的聚合距离蒸馏到重建分数网络中，随后在通过蒸馏模型过滤的干净数据上进行微调，实现异常的精确定位。MeDS在广泛的噪声比例下具有鲁棒性，无需针对特定噪声比例的超参数调整，在MVTecAD上以40%噪声比例达到99.16%的图像级AUROC，并在噪声设置下在VisA和Real-IAD上取得最先进性能。我们在噪声数据场景下的工业AD基准上彻底验证了MeDS的有效性，并进行了深入的经验分析。
英文摘要：
Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfectly clean training sets is impractical. However, existing methods are sensitive to contamination, suffering significant performance degradation as the noise ratio increases. In this paper, we propose Memory-Distilled Selection (MeDS), a training algorithm based on data selection. MeDS constructs an ensemble of partial memories via random subsampling, where the resulting sparsity acts as a low-pass filter that captures nominal patterns across a wide range of noise ratios, enabling coarse-level identification of contaminated samples. The aggregated distances to the bootstrapped memories are then distilled into a reconstruction score network, which is subsequently fine-tuned on clean data filtered using scores from the distilled model, enabling fine-grained localization of anomalies. MeDS is robust across a wide range of noise ratios without requiring noise-ratio-specific hyperparameter tuning, achieving 99.16\% image-level AUROC on MVTecAD at a 40\% noise ratio, and attaining state-of-the-art performance on both VisA and Real-IAD under noisy settings. We thoroughly verify the efficacy of MeDS on industrial AD benchmarks under noisy data scenarios, accompanied by in-depth empirical analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
