HetNet uses a frozen CNN and Transformer teacher pair, attention-based feature fusion, and multivariate Gaussian noise in feature space to detect surface defects robustly under variable industrial imaging conditions.
Mvtec ad– a comprehensive real-world dataset for unsupervised anomaly detec- tion,
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Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
HetNet uses a frozen CNN and Transformer teacher pair, attention-based feature fusion, and multivariate Gaussian noise in feature space to detect surface defects robustly under variable industrial imaging conditions.