Abstract
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.
Abstract (translated)
所谓的无监督异常检测实际上更接近于半监督学习，因为它假设所有训练数据都是正常的。这种假设虽然简化了模型的训练过程，但需要手动进行数据整理以去除或标注异常样本，这不仅引入了偏差，还限制了模型适应新数据的能力。我们提出了一种新的训练策略——自信元学习（Confident Meta-learning, CoMet），这种方法可以使深度异常检测模型从未经整理的数据集中学习，在这些数据集中正常和异常样本共存。CoMet消除了显式过滤的需要，使得模型能够更加灵活地处理包含多种类型样本的真实世界数据。 我们的方法结合了软自信学习（Soft Confident Learning）技术，该技术对置信度较低的样本赋予较小的权重，并使用元学习（Meta-Learning）来通过基于训练验证损失协方差的正则化更新规则稳定模型训练。这种方法不仅能够防止过拟合现象，还能增强模型在处理嘈杂数据时的鲁棒性。 CoMet算法与特定的模型无关，可以应用于任何可以通过梯度下降进行训练的异常检测方法中。我们通过在MVTec-AD、VIADUCT和KSDD2数据集上使用两种最先进的模型进行了实验验证，结果表明我们的方法能够显著优于基准方法，并且不受训练集中存在的异常样本的影响，在所有测试的数据集中均取得了新的最优性能。
URL
https://arxiv.org/abs/2508.02293
