让异常检测模型无需人工筛选数据，直接在混有异常的原始数据中训练。
Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning
- 用置信度加权机制过滤低置信样本，结合元学习稳定训练过程。
- 在三个数据集上均超越基线，对训练集中的异常不敏感且性能领先。
- 适用于任何梯度可训练的异常检测模型，适合真实场景部署。
所谓无监督异常检测实为半监督，因其假设训练数据全为正常样本。此假设虽简化训练，但依赖人工数据清洗，引入偏差并限制适应性。本文提出置信元学习（CoMet），一种新训练策略，使深度异常检测模型能从未经清理的数据中学习，其中正常与异常样本共存，无需显式过滤。方法融合软置信学习（降低低置信样本权重）与元学习（基于训练验证损失协方差正则化更新），防止过拟合并提升对噪声数据的鲁棒性。CoMet具备模型无关性，可应用于任意可通过梯度下降训练的异常检测方法。在MVTec-AD、VIADUCT和KSDD2三个数据集上，使用两种先进模型验证，结果表明该方法持续优于基线，对训练集中的异常不敏感，并在所有数据集上达到新的最优水平。代码已开源：https://github.com/aqeeelmirza/CoMet。
原文摘要 · Abstract (English)
So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manual data curation, introducing bias and limiting adaptability. We propose Confident Meta-learning (CoMet), a novel training strategy that enables deep anomaly detection models to learn from uncurated datasets where nominal and anomalous samples coexist, eliminating the need for explicit filtering. Our approach integrates Soft Confident Learning, which assigns lower weights to low-confidence samples, and Meta-Learning, which stabilizes training by regularizing updates based on training validation loss covariance. This prevents overfitting and enhances robustness to noisy data. CoMet is model-agnostic and can be applied to any anomaly detection method trainable via gradient descent. Experiments on MVTec-AD, VIADUCT, and KSDD2 with two state-of-the-art models demonstrate the effectiveness of our approach, consistently improving over the baseline methods, remaining insensitive to anomalies in the training set, and setting a new state-of-the-art across all datasets. Code is available at https://github.com/aqeeelmirza/CoMet
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