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 2701–2710 of 4856 papers

Title	Date	Tasks	Status
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients	Feb 27, 2023	Anomaly DetectionLesion Detection	—Unverified
Interruptions detection in video conferences	Feb 25, 2023	Anomaly Detection	CodeCode Available
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection	Feb 24, 2023	Anomaly DetectionImage Inpainting	—Unverified
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems	Feb 24, 2023	Anomaly Detection	CodeCode Available
Explainable Contextual Anomaly Detection using Quantile Regression Forests	Feb 22, 2023	Anomaly DetectionContextual Anomaly Detection	CodeCode Available
Using Semantic Information for Defining and Detecting OOD Inputs	Feb 21, 2023	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning	Feb 21, 2023	Anomaly DetectionContrastive Learning	—Unverified
CNTS: Cooperative Network for Time Series	Feb 20, 2023	Anomaly DetectionTime Series	CodeCode Available
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection	Feb 20, 2023	Anomaly DetectionDecoder	—Unverified
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine	Feb 18, 2023	Anomaly Detection	—Unverified

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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