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 1981–1990 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection in Networks via Score-Based Generative Models	Jun 27, 2023	Anomaly DetectionOutlier Detection	CodeCode Available	0
Precursor-of-Anomaly Detection for Irregular Time Series	Jun 27, 2023	Anomaly DetectionIrregular Time Series	CodeCode Available	1
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection	Jun 26, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Anomaly Detection with Score Distribution Discrimination	Jun 26, 2023	Anomaly Detection	CodeCode Available	1
Autoencoders for Real-Time SUEP Detection	Jun 23, 2023	Anomaly Detection	—Unverified	0
OptIForest: Optimal Isolation Forest for Anomaly Detection	Jun 22, 2023	Anomaly DetectionBenchmarking	CodeCode Available	0
Triggering Dark Showers with Conditional Dual Auto-Encoders	Jun 22, 2023	Anomaly Detection	CodeCode Available	0
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center	Jun 22, 2023	Anomaly Detection	—Unverified	0
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection	Jun 21, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection	Jun 21, 2023	Anomaly DetectionData Augmentation	—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