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 91–100 of 4856 papers

Title	Date	Tasks	Status	Hype
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis	Aug 14, 2024	Anomaly DetectionBoundary Detection	CodeCode Available	2
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features	Jul 17, 2024	Anomaly DetectionSelf-Driving Cars	CodeCode Available	2
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation	Jul 17, 2024	Anomaly DetectionAnomaly Segmentation	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
Odd-One-Out: Anomaly Detection by Comparing with Neighbors	Jun 28, 2024	8kAnomaly Detection	CodeCode Available	2
European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry	Jun 25, 2024	Anomaly DetectionTime Series	CodeCode Available	2
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark	Jun 21, 2024	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	2
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM	Jun 18, 2024	Anomaly DetectionAnomaly Localization	CodeCode Available	2
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection	Jun 11, 2024	Anomaly DetectionDenoising	CodeCode Available	2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2	May 23, 2024	Anomaly DetectionAnomaly Segmentation	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