Agree to Disagree: Robust Anomaly Detection with Noisy Labels
Summary: Unity: LNL for anomaly detection uniting sample selection and label refurbishment. Dual nets agree to pick clean labels, resolve disagreements for marginal cases, and apply anomaly-centric contrastive learning to refurbish the rest; iterative training yields strong F1 gains on 10 real datasets. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Dennis M. Hofmann
- 2. Peter M. VanNostrand
- 3. Lei Ma
- 4. Huayi Zhang
- 5. Joshua C. DeOliveira
- 6. Lei Cao
- 7. Elke A. Rundensteiner
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,126 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.4887794e-05 |
| 2,381 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 8.9327638e-05 |
| 4,154 | Robust and Transferable Log-based Anomaly Detection | 2023 | SIGMOD | 6.4032498e-05 |
| 4,456 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD | 6.1704203e-05 |
| 4,911 | Unsupervised Contextual Anomaly Detection for Database Systems | 2022 | SIGMOD | 5.8328593e-05 |
| 8,714 | LANCET: Labeling Complex Data at Scale | 2021 | VLDB | 4.4619818e-05 |
| 11,000 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB | 4.1945683e-05 |
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