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 2111–2120 of 4856 papers

Title	Date	Tasks	Status	Hype
Interactive System-wise Anomaly Detection	Apr 21, 2023	Anomaly DetectionData Poisoning	—Unverified	0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies	Apr 20, 2023	Anomaly DetectionDenoising	—Unverified	0
Weakly Supervised Detection of Baby Cry	Apr 19, 2023	Anomaly DetectionSupervised Anomaly Detection	—Unverified	0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection	Apr 19, 2023	Activity RecognitionAnomaly Detection	—Unverified	0
Graph Neural Network-Based Anomaly Detection for River Network Systems	Apr 19, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities	Apr 17, 2023	Anomaly DetectionLesion Detection	—Unverified	0
Long-term Forecasting with TiDE: Time-series Dense Encoder	Apr 17, 2023	Anomaly DetectionDecoder	CodeCode Available	5
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection	Apr 16, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN	Apr 16, 2023	Anomaly DetectionOdd One Out	CodeCode Available	1
Context-aware Domain Adaptation for Time Series Anomaly Detection	Apr 15, 2023	Anomaly DetectionDeep Reinforcement Learning	—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