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 2741–2750 of 4856 papers

Title	Date	Tasks	Status	Hype
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms	Jun 6, 2022	Anomaly Detection	—Unverified	0
[Reproducibility Report] Explainable Deep One-Class Classification	Jun 6, 2022	Anomaly DetectionClassification	—Unverified	0
Perturbation Learning Based Anomaly Detection	Jun 6, 2022	Anomaly Detection	—Unverified	0
Anomaly Detection with Test Time Augmentation and Consistency Evaluation	Jun 6, 2022	Anomaly DetectionRepresentation Learning	—Unverified	0
A Three-Stage Anomaly Detection Framework for Traffic Videos	Jun 5, 2022	Anomaly Detection	CodeCode Available	0
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks	Jun 4, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Anomaly detection in surveillance videos using transformer based attention model	Jun 3, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Invertible Neural Networks for Graph Prediction	Jun 2, 2022	Anomaly DetectionGraph Neural Network	CodeCode Available	0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models	Jun 1, 2022	Anomaly DetectionNatural Language Inference	—Unverified	0
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )	Jun 1, 2022	Anomaly DetectionPrompt Engineering	—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	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