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 4411–4420 of 4856 papers

Title	Date	Tasks	Status	Hype
Mapper Comparison with Wasserstein Metrics	Dec 15, 2018	Anomaly DetectionOpen-Ended Question Answering	CodeCode Available	0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection	Dec 14, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation	Dec 14, 2018	Anomaly DetectionDensity Estimation	—Unverified	0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT	Dec 12, 2018	Anomaly Detection	—Unverified	0
Real-Time Anomaly Detection With HMOF Feature	Dec 12, 2018	Anomaly DetectionClustering	—Unverified	0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video	Dec 11, 2018	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection	Dec 11, 2018	Anomaly DetectionIntrusion Detection	—Unverified	0
Deep Anomaly Detection with Outlier Exposure	Dec 11, 2018	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	1
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks	Dec 7, 2018	Anomaly DetectionDiagnostic	—Unverified	0
Adversarially Learned Anomaly Detection	Dec 6, 2018	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	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