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Found 4 papers, 1 papers with code
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise
To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them in this work.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation
Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting.
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