AF3AD is a modular synthesis framework using center-conditioned parametric deformations in local PCA frames to create diverse pseudo-anomalies, improving unsupervised 3D anomaly detection on AnomalyShapeNet and Real3D-AD.
In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
2 Pith papers cite this work. Polarity classification is still indexing.
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HADES adapts knowledge distillation for hypergraph neural networks by using quantified node heterophily as a proxy for teacher reliability, yielding student models that often outperform teachers with up to 12.3x faster inference.
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Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks
HADES adapts knowledge distillation for hypergraph neural networks by using quantified node heterophily as a proxy for teacher reliability, yielding student models that often outperform teachers with up to 12.3x faster inference.