Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning
This addresses the need for more adaptable and less biased anomaly detection in real-world applications by removing the requirement for clean training data.
The paper tackles the problem of unsupervised anomaly detection by proposing Confident Meta-learning (CoMet), a training strategy that allows models to learn from uncurated datasets with mixed nominal and anomalous samples, eliminating manual filtering. Experiments on datasets like MVTec-AD show it consistently improves over baselines and sets a new state-of-the-art.
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