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 2231–2240 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems	Feb 24, 2023	Anomaly Detection	CodeCode Available	0
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection	Feb 24, 2023	Anomaly DetectionImage Inpainting	—Unverified	0
Set Features for Fine-grained Anomaly Detection	Feb 23, 2023	Anomaly DetectionTime Series	CodeCode Available	1
One Fits All:Power General Time Series Analysis by Pretrained LM	Feb 23, 2023	Anomaly DetectionFew-Shot Learning	CodeCode Available	2
Explainable Contextual Anomaly Detection using Quantile Regression Forests	Feb 22, 2023	Anomaly DetectionContextual Anomaly Detection	CodeCode Available	0
Using Semantic Information for Defining and Detecting OOD Inputs	Feb 21, 2023	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified	0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning	Feb 21, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Memory-augmented Online Video Anomaly Detection	Feb 21, 2023	Anomaly DetectionAutonomous Vehicles	CodeCode Available	1
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis	Feb 20, 2023	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection	Feb 20, 2023	Anomaly DetectionDecoder	—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