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 2301–2310 of 4856 papers

Title	Date	Tasks	Status
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference	Oct 23, 2023	Anomaly DetectionDomain Adaptation	—Unverified
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots	Oct 21, 2023	Anomaly DetectionManagement	—Unverified
Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches	Oct 20, 2023	Anomaly Detection	—Unverified
An Event based Prediction Suffix Tree	Oct 20, 2023	Anomaly DetectionOne-Shot Learning	—Unverified
Positive-Unlabeled Node Classification with Structure-aware Graph Learning	Oct 20, 2023	Anomaly DetectionClassification	—Unverified
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches	Oct 20, 2023	Anomaly DetectionComputer Security	—Unverified
A New Time Series Similarity Measure and Its Smart Grid Applications	Oct 19, 2023	Anomaly DetectionDynamic Time Warping	—Unverified
SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation	Oct 19, 2023	Anomaly DetectionManagement	—Unverified
VALD-GAN: video anomaly detection using latent discriminator augmented GAN	Oct 18, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Open-Set Multivariate Time-Series Anomaly Detection	Oct 18, 2023	Anomaly DetectionContrastive Learning	—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	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