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 1341–1350 of 4856 papers

Title	Date	Tasks	Status	Score
From Zero to Hero: Cold-Start Anomaly Detection	May 30, 2024	Anomaly DetectionCold-Start Anomaly Detection	CodeCode Available	5
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features	Oct 26, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	5
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms	Feb 25, 2025	Anomaly Detection	CodeCode Available	5
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection	Jun 21, 2024	Anomaly DetectionEvent Detection	CodeCode Available	5
Concept Drift and Anomaly Detection in Graph Streams	Jun 21, 2017	Anomaly DetectionChange Detection	CodeCode Available	5
A Meta-Analysis of the Anomaly Detection Problem	Mar 3, 2015	Anomaly DetectionBenchmarking	CodeCode Available	5
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams	Jun 6, 2018	Anomaly DetectionDeep Learning	CodeCode Available	5
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking	Apr 7, 2021	Anomaly DetectionInformation Retrieval	CodeCode Available	5
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations	Mar 15, 2024	Anomaly DetectionDecoder	CodeCode Available	5
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training	May 17, 2018	Anomaly DetectionDecoder	CodeCode Available	5

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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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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