A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
It addresses data scarcity and annotation costs in evolving manufacturing and healthcare environments, offering a resource-efficient solution.
The paper tackles zero-shot anomaly detection in industrial and medical settings by proposing CoZAD, a framework that integrates confident meta-learning and contrastive learning, achieving state-of-the-art performance with improvements such as 99.2% I-AUROC on DTD-Synthetic and 96.3% P-AUROC on MVTec-AD.
Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settings. To address this, we propose CoZAD, a novel zero-shot anomaly detection framework that integrates soft confident learning with meta-learning and contrastive feature representation. Unlike traditional confident learning that discards uncertain samples, our method assigns confidence-based weights to all training data, preserving boundary information while emphasizing prototypical normal patterns. The framework quantifies data uncertainty through IQR-based thresholding and model uncertainty via covariance based regularization within a Model-Agnostic Meta-Learning. Contrastive learning creates discriminative feature spaces where normal patterns form compact clusters, enabling rapid domain adaptation. Comprehensive evaluation across 10 datasets spanning industrial and medical domains demonstrates state-of-the-art performance, outperforming existing methods on 6 out of 7 industrial benchmarks with notable improvements on texture-rich datasets (99.2% I-AUROC on DTD-Synthetic, 97.2% on BTAD) and pixellevel localization (96.3% P-AUROC on MVTec-AD). The framework eliminates dependence on vision-language alignments or model ensembles, making it valuable for resourceconstrained environments requiring rapid deployment.