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 2911–2920 of 4856 papers

Title	Date	Tasks	Status
InQMAD: Incremental Quantum Measurement Anomaly Detection	Oct 11, 2022	Anomaly DetectionDensity Estimation	CodeCode Available
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning	Oct 11, 2022	Anomaly DetectionMulti-agent Reinforcement Learning	CodeCode Available
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations	Oct 9, 2022	Anomaly Detectioncounterfactual	—Unverified
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!	Oct 6, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Anomaly detection using data depth: multivariate case	Oct 6, 2022	Anomaly Detection	—Unverified
Null Hypothesis Test for Anomaly Detection	Oct 5, 2022	Anomaly Detection	CodeCode Available
Improved Anomaly Detection by Using the Attention-Based Isolation Forest	Oct 5, 2022	Anomaly Detection	CodeCode Available
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets	Oct 4, 2022	Anomaly DetectionMultiple Instance Learning	—Unverified
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning	Oct 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available
Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback	Oct 1, 2022	Anomaly DetectionContrastive Learning	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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