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 1841–1850 of 4856 papers

Title	Date	Tasks	Status	Hype
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection	Aug 22, 2023	Anomaly DetectionIntrusion Detection	—Unverified	0
Few-shot Anomaly Detection in Text with Deviation Learning	Aug 22, 2023	Anomaly DetectionFew-Shot Learning	—Unverified	0
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection	Aug 22, 2023	Anomaly DetectionBinary Classification	CodeCode Available	2
Class Label-aware Graph Anomaly Detection	Aug 22, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection	Aug 22, 2023	Anomaly DetectionData Augmentation	—Unverified	0
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection	Aug 21, 2023	Anomaly DetectionAttribute	CodeCode Available	1
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection	Aug 21, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	0
Inferring Power Grid Information with Power Line Communications: Review and Insights	Aug 21, 2023	Anomaly Detection	—Unverified	0
Adaptive Thresholding Heuristic for KPI Anomaly Detection	Aug 21, 2023	Anomaly DetectionTime Series	CodeCode Available	0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection	Aug 20, 2023	Anomaly DetectionContrastive Learning	—Unverified	0

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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