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 2881–2890 of 4856 papers

Title	Date	Tasks	Status
Spatio-temporal predictive tasks for abnormal event detection in videos	Oct 27, 2022	Anomaly DetectionEvent Detection	—Unverified
A Hierarchical Approach to Conditional Random Fields for System Anomaly Detection	Oct 26, 2022	Anomaly Detection	—Unverified
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features	Oct 26, 2022	Anomaly DetectionDensity Estimation	CodeCode Available
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection	Oct 26, 2022	Anomaly DetectionAnomaly Segmentation	—Unverified
Detecting fake accounts through Generative Adversarial Network in online social media	Oct 25, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions	Oct 25, 2022	Anomaly DetectionBenchmarking	—Unverified
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly Detection	Oct 25, 2022	Anomaly Detection	CodeCode Available
Bridging Machine Learning and Sciences: Opportunities and Challenges	Oct 24, 2022	Anomaly DetectionOut-of-Distribution Detection	—Unverified
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation	Oct 23, 2022	Anomaly DetectionRepresentation Learning	—Unverified
Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data	Oct 21, 2022	Anomaly DetectionGPR	—Unverified

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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