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 4091–4100 of 4856 papers

Title	Date	Tasks	Status
Learning Representations for Time Series Clustering	Dec 1, 2019	Anomaly DetectionClustering	CodeCode Available
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler	Apr 3, 2020	Anomaly Detection	CodeCode Available
TracInAD: Measuring Influence for Anomaly Detection	May 3, 2022	Anomaly Detection	CodeCode Available
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos	Mar 8, 2019	Anomaly DetectionDecoder	CodeCode Available
Learning normal asymmetry representations for homologous brain structures	Jun 27, 2023	Anomaly DetectionRepresentation Learning	CodeCode Available
Anomaly detection with superexperts under delayed feedback	Oct 8, 2020	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Learning Neural Representations for Network Anomaly Detection	Aug 1, 2019	Anomaly DetectionIntrusion Detection	CodeCode Available
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection	Jun 1, 2018	Anomaly DetectionImage Generation	CodeCode Available
Wildfire danger prediction optimization with transfer learning	Mar 19, 2024	Anomaly Detectionobject-detection	CodeCode Available
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport	Feb 18, 2025	Anomaly DetectionFault Detection	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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