Cross-dataset testing of nearest-neighbor and Mahalanobis anomaly detectors on CLIP, DINOv2, ResNet-50 and EfficientNet embeddings shows same-dataset AUC averaging 0.704 dropping to 0.499 on other datasets, with false-alarm rates around 31,931 per hour at usable operating points.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection,
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A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.