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 141–150 of 4856 papers

Title	Date	Tasks	Status	Hype
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching	Oct 16, 2024	Anomaly DetectionTime Series	CodeCode Available	2
European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry	Jun 25, 2024	Anomaly DetectionTime Series	CodeCode Available	2
FITS: Modeling Time Series with 10k Parameters	Jul 6, 2023	Anomaly DetectionTime Series	CodeCode Available	2
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models	Jul 14, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	2
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features	Jul 17, 2024	Anomaly DetectionSelf-Driving Cars	CodeCode Available	2
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection	Oct 29, 2023	Anomaly DetectionPrompt Learning	CodeCode Available	2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering	May 2, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding	Feb 13, 2018	Anomaly Detection	CodeCode Available	2
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization	Apr 21, 2024	Anomaly DetectionPosition	CodeCode Available	2
Log-based Anomaly Detection with Deep Learning: How Far Are We?	Feb 9, 2022	Anomaly DetectionDeep Learning	CodeCode Available	2

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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