AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
Generative adversarial networks: An overview
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APKH uses prompt-optimized attribute kernel mapping and kernel-smoothed contrastive alignment to improve generalization from seen to unseen categories in data-constrained unsupervised cross-modal hashing.
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Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
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Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval
APKH uses prompt-optimized attribute kernel mapping and kernel-smoothed contrastive alignment to improve generalization from seen to unseen categories in data-constrained unsupervised cross-modal hashing.