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October 20, 2025Open Access

Generalist Multi-Class Anomaly Detection via Distillation to Two Heterogeneous Student Networks

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This dual-model ensemble improves anomaly detection in industrial and semantic contexts, indicating broader applications.

The proposed method achieved state-of-the-art accuracies in both industrial and semantic anomaly detection tasks.
Our dual-model ensemble framework utilized knowledge distillation for enhanced performance across multiple benchmarks.
We achieved an image-level AUROC of 99.7% on MVTec-AD, outperforming prior general AD models significantly.
The model demonstrates remarkable generalization capabilities across various anomaly detection domains and settings.

Park et al. (2025) studied this question.

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October 20, 2025Open Access

View Full Paper

Ask AI

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Authors

This dual-model ensemble improves anomaly detection in industrial and semantic contexts, indicating broader applications.

The proposed method achieved state-of-the-art accuracies in both industrial and semantic anomaly detection tasks.
Our dual-model ensemble framework utilized knowledge distillation for enhanced performance across multiple benchmarks.
We achieved an image-level AUROC of 99.7% on MVTec-AD, outperforming prior general AD models significantly.
The model demonstrates remarkable generalization capabilities across various anomaly detection domains and settings.

Park et al. (2025) studied this question.

View Full Paper

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