A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection

• URL: http://arxiv.org/abs/2508.17827v1
• Date: Mon, 25 Aug 2025 09:27:31 GMT
• Title: A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
• Authors: Muhammad Aqeel, Danijel Skocaj, Marco Cristani, Francesco Setti,
• Abstract summary: CoZAD is a novel zero-shot anomaly detection framework.<n>It integrates soft confident learning with meta-learning and contrastive feature representation.<n>We show it outperforms existing methods on 6 out of 7 industrial benchmarks.
• Score: 17.73056562717683
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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