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 701–710 of 4856 papers

Title	Date	Tasks	Status	Hype
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection	Aug 17, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
Graph Contrastive Learning for Anomaly Detection	Aug 17, 2021	Anomaly DetectionBinary Classification	CodeCode Available	1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction	Aug 16, 2021	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening	Aug 9, 2021	Anomaly DetectionVariational Inference	CodeCode Available	1
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature Mapping	Aug 6, 2021	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Log-based Anomaly Detection Without Log Parsing	Aug 4, 2021	Anomaly DetectionLog Parsing	CodeCode Available	1
Explainable Deep Few-shot Anomaly Detection with Deviation Networks	Aug 1, 2021	Anomaly DetectionMultiple Instance Learning	CodeCode Available	1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows	Jul 27, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation	Jul 23, 2021	Anomaly DetectionScene Segmentation	CodeCode Available	1
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision	Jul 16, 2021	3D ReconstructionAnomaly Detection	CodeCode Available	1

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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