Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3651–3660 of 4856 papers

Title	Date	Tasks	Status
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions	Mar 26, 2025	Anomaly DetectionLanguage Modeling	—Unverified
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction	Aug 23, 2022	Anomaly Detectiongraph construction	—Unverified
LogLLaMA: Transformer-based log anomaly detection with LLaMA	Mar 19, 2025	Anomaly DetectionReinforcement Learning (RL)	—Unverified
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU	Nov 20, 2019	Anomaly DetectionClassification	—Unverified
LogRCA: Log-based Root Cause Analysis for Distributed Services	May 22, 2024	Anomaly Detection	—Unverified
LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysis	Oct 29, 2024	Anomaly DetectionCyber Attack Detection	—Unverified
Long-Tailed Anomaly Detection with Learnable Class Names	Mar 29, 2024	Anomaly Detection	—Unverified
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning	Jan 1, 2023	Anomaly DetectionRelational Reasoning	—Unverified
Look at Adjacent Frames: Video Anomaly Detection without Offline Training	Jul 27, 2022	Anomaly DetectionVideo Anomaly Detection	—Unverified
Looking for Tiny Defects via Forward-Backward Feature Transfer	Jul 4, 2024	Anomaly DetectionSegmentation	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified