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 1321–1330 of 4856 papers

Title	Date	Tasks	Status	Hype
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data	Mar 24, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components	Mar 21, 2024	Anomaly DetectionVideo Segmentation	—Unverified	0
A task of anomaly detection for a smart satellite Internet of things system	Mar 21, 2024	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients	Mar 21, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	0
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection	Mar 21, 2024	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	2
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond	Mar 21, 2024	Anomaly DetectionDeep Learning	CodeCode Available	3
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference	Mar 21, 2024	Anomaly DetectionDecoder	—Unverified	0
SoftPatch: Unsupervised Anomaly Detection with Noisy Data	Mar 21, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection	Mar 21, 2024	Anomaly DetectionMedical Image Analysis	CodeCode Available	0
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection	Mar 20, 2024	Anomaly DetectionMulti-class Anomaly 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