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 4171–4180 of 4856 papers

Title	Date	Tasks	Status
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space	Oct 10, 2019	Anomaly DetectionRelation	—Unverified
A Joint Model for IT Operation Series Prediction and Anomaly Detection	Oct 9, 2019	Anomaly DetectionPrediction	—Unverified
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure	Oct 9, 2019	Anomaly DetectionFraud Detection	CodeCode Available
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes	Oct 7, 2019	Anomaly DetectionDemand Forecasting	CodeCode Available
AKM^2D : An Adaptive Framework for Online Sensing and Anomaly Quantification	Oct 4, 2019	Anomaly Detection	—Unverified
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data	Oct 2, 2019	Anomaly DetectionDimensionality Reduction	—Unverified
Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes	Oct 1, 2019	Anomaly DetectionAutonomous Driving	—Unverified
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence	Oct 1, 2019	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Active Anomaly Detection for time-domain discoveries	Sep 29, 2019	Active LearningAnomaly Detection	—Unverified
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods	Sep 26, 2019	Anomaly DetectionClassification	—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	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