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: 7007
Venue: SIGMOD
Year: 2025
Pagerank: 4.1905499e-05
Overall Rank: 10,377 | 27.88%
DOI: 10.1145/3709657

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,129	MacroBase: Prioritizing Attention in Fast Data	2017	SIGMOD	9.4799835e-05
2,381	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	8.9241557e-05
4,155	Robust and Transferable Log-based Anomaly Detection	2023	SIGMOD	6.3970893e-05
4,455	AutoOD: Automatic Outlier Detection	2023	SIGMOD	6.1644904e-05
4,917	Unsupervised Contextual Anomaly Detection for Database Systems	2022	SIGMOD	5.8272504e-05
8,713	LANCET: Labeling Complex Data at Scale	2021	VLDB	4.4577046e-05
11,003	MisDetect: Iterative Mislabel Detection using Early Loss	2024	VLDB	4.1905499e-05

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