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
Coincident Learning for Unsupervised Anomaly Detection	Jan 26, 2023	Anomaly DetectionTime Series	—Unverified
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles	Jan 25, 2023	Anomaly DetectionClustering	—Unverified
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning	Jan 25, 2023	Anomaly Detection	—Unverified
Unravelling physics beyond the standard model with classical and quantum anomaly detection	Jan 25, 2023	Anomaly Detection	—Unverified
A convolutional neural network of low complexity for tumor anomaly detection	Jan 24, 2023	Anomaly DetectionClassification	—Unverified
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing	Jan 24, 2023	Anomaly DetectionDeep Learning	—Unverified
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation	Jan 23, 2023	Anomaly DetectionAnomaly Localization	—Unverified
Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection	Jan 23, 2023	Anomaly DetectionOpen Set Learning	CodeCode Available
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference	Jan 23, 2023	Anomaly DetectionImage Generation	—Unverified
Condition monitoring and anomaly detection in cyber-physical systems	Jan 22, 2023	Anomaly Detection	—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	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