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 1381–1390 of 4856 papers

Title	Date	Tasks	Status	Score
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms	Feb 25, 2025	Anomaly Detection	CodeCode Available	5
DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models	Jun 22, 2024	Anomaly Detection	CodeCode Available	5
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories	Mar 17, 2023	Anomaly DetectionGroup Anomaly Detection	CodeCode Available	5
Finite sample guarantees for quantile estimation: An application to detector threshold tuning	May 25, 2021	Anomaly Detection	CodeCode Available	5
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path Signature	Jun 5, 2020	Anomaly DetectionTime Series	CodeCode Available	5
Comparison of Anomaly Detectors: Context Matters	Dec 11, 2020	Anomaly DetectionModel Selection	CodeCode Available	5
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks	Sep 22, 2022	Anomaly DetectionImputation	CodeCode Available	5
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification	Feb 8, 2022	Anomaly DetectionBenchmarking	CodeCode Available	5
Few-shot Online Anomaly Detection and Segmentation	Mar 27, 2024	Anomaly Detection	CodeCode Available	5
Fence GAN: Towards Better Anomaly Detection	Apr 2, 2019	Anomaly ClassificationAnomaly Detection	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