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 3311–3320 of 4856 papers

Title	Date	Tasks	Status	Hype
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation	Jun 20, 2021	Anomaly DetectionDensity Estimation	CodeCode Available	0
Large-Scale Network Embedding in Apache Spark	Jun 20, 2021	Anomaly DetectionLink Prediction	—Unverified	0
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison	Jun 19, 2021	Anomaly DetectionAnomaly Localization	—Unverified	0
Deep Learning in Latent Space for Video Prediction and Compression	Jun 19, 2021	Anomaly DetectionDeep Learning	CodeCode Available	1
TS2Vec: Towards Universal Representation of Time Series	Jun 19, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection	Jun 18, 2021	Anomaly DetectionCombinatorial Optimization	CodeCode Available	0
Anomaly Detection in Dynamic Graphs via Transformer	Jun 18, 2021	Anomaly Detection	CodeCode Available	1
Anomaly Detection and Automated Labeling for Voter Registration File Changes	Jun 16, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions	Jun 16, 2021	Anomaly DetectionAutonomous Vehicles	—Unverified	0
Anomaly Detection in Video Sequences: A Benchmark and Computational Model	Jun 16, 2021	Anomaly Detection	CodeCode Available	1

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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