Vendi Anomaly Scores for Efficient and Accurate Anomaly Detection
- Published
- Source
- arXiv
- Paper number
- 1442
- Field
- Machine Learning
- arXiv ID
- 2611.12871
Key points
- The authors propose the Vendi Anomaly Score, which defines anomalies by how much removing a sample changes dataset diversity measured by the Vendi score.
- They derive a first-order approximation to the exact leave-one-out score so the method scales linearly with dataset size.
- The method requires no density estimation, partitioning heuristics, or hyperparameter tuning, adapting q automatically to the spectral structure of the data.
- It achieves state-of-the-art anomaly detection performance across 148 benchmark datasets derived from 10 source datasets and 4 embedding architectures.
- It remains robust in ImageNet experiments with up to 100k samples as both contamination rate and dataset scale increase.
Paper links
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