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Found 3 papers, 2 papers with code
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
However, the anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples, which hinders their ability to detect real anomalies.
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method.
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