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SotA/Methodology/Anomaly Detection/AnoShift

Anomaly Detection on AnoShift

Metric: ROC-AUC-ID (In-Distribution setup) (higher is better)

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#Model↕ROC-AUC-ID (In-Distribution setup)▼AugmentationsPaperDate↕Code
1deepSVDD88.24NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
2LOF87.61NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
3IsoForest81.27NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
4COPOD80.89NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
5BERT79.62NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
6ECOD Li et al. (2022)79.41NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
7LUNAR78.53NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
8OC-SVM68.73NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
9Internal Contrastive Learning66.99NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
10AE for anomalies64.08NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code
11SO-GAAL49.9NoAnoShift: A Distribution Shift Benchmark for Uns...2022-06-30Code