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 4441–4450 of 4856 papers

Title	Date	Tasks	Status
Generative Neural Networks for Anomaly Detection in Crowded Scenes	Oct 29, 2018	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection	Mar 17, 2022	Activity PredictionAnomaly Detection	CodeCode Available
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features	Apr 13, 2018	Anomaly DetectionDecision Making	CodeCode Available
Network Traffic Anomaly Detection Using Recurrent Neural Networks	Mar 28, 2018	Anomaly DetectionLanguage Modeling	CodeCode Available
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure	Oct 9, 2019	Anomaly DetectionFraud Detection	CodeCode Available
Scalable Motif Counting for Large-scale Temporal Graphs	Apr 20, 2022	Anomaly DetectionCPU	CodeCode Available
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines	Aug 5, 2024	Anomaly Detectionobject-detection	CodeCode Available
Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories	Sep 27, 2024	Anomaly DetectionCollaborative Filtering	CodeCode Available
General Domain Adaptation Through Proportional Progressive Pseudo Labeling	Dec 23, 2020	Anomaly DetectionDomain Adaptation	CodeCode Available
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider	May 28, 2021	Anomaly DetectionDensity Estimation	CodeCode Available

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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