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	Hype
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning	Jan 25, 2023	Anomaly Detection	—Unverified	0
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing	Jan 24, 2023	Anomaly DetectionDeep Learning	—Unverified	0
A convolutional neural network of low complexity for tumor anomaly detection	Jan 24, 2023	Anomaly DetectionClassification	—Unverified	0
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	0
Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection	Jan 23, 2023	Anomaly DetectionOpen Set Learning	CodeCode Available	0
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation	Jan 23, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
Condition monitoring and anomaly detection in cyber-physical systems	Jan 22, 2023	Anomaly Detection	—Unverified	0
The role of noise in denoising models for anomaly detection in medical images	Jan 19, 2023	Anomaly DetectionDenoising	CodeCode Available	1
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection	Jan 19, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Position Regression for Unsupervised Anomaly Detection	Jan 19, 2023	Anomaly DetectionImage Reconstruction	CodeCode Available	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	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