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 3961–3970 of 4856 papers

Title	Date	Tasks	Status	Hype
A Kernel Two-sample Test for Dynamical Systems	Apr 23, 2020	Anomaly DetectionFeature Engineering	—Unverified	0
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection	Apr 22, 2020	Anomaly Detection	—Unverified	0
Sequential Anomaly Detection using Inverse Reinforcement Learning	Apr 22, 2020	Anomaly DetectionDecision Making	—Unverified	0
Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection	Apr 20, 2020	Anomaly Detection	CodeCode Available	0
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning	Apr 20, 2020	Anomaly DetectionDisentanglement	CodeCode Available	0
Network Anomaly Detection based on Tensor Decomposition	Apr 20, 2020	Anomaly DetectionTensor Decomposition	—Unverified	0
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture	Apr 18, 2020	Anomaly DetectionClassification	CodeCode Available	0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series	Apr 17, 2020	Anomaly DetectionOutlier Detection	—Unverified	0
Exploring time-series motifs through DTW-SOM	Apr 17, 2020	Anomaly DetectionClustering	—Unverified	0
Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging	Apr 17, 2020	Anomaly DetectionOptical Flow Estimation	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