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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)

Paper ID
7006
Venue
SIGMOD
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,365 | 27.90%
DOI
10.1145/3709657

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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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