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 2201–2210 of 4856 papers

Title	Date	Tasks	Status	Hype
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency	Mar 9, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection	Mar 9, 2023	Anomaly DetectionPose-based Anomaly Detection	—Unverified	0
Region and Spatial Aware Anomaly Detection for Fundus Images	Mar 7, 2023	Anomaly Detection	—Unverified	0
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT	Mar 7, 2023	Anomaly DetectionTime Series	—Unverified	0
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI	Mar 7, 2023	AnatomyAnomaly Detection	CodeCode Available	1
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams	Mar 7, 2023	Anomaly DetectionData Compression	CodeCode Available	1
Memory Maps for Video Object Detection and Tracking on UAVs	Mar 6, 2023	Anomaly DetectionMulti-Object Tracking	—Unverified	0
Time series anomaly detection with reconstruction-based state-space models	Mar 6, 2023	Anomaly DetectionDecoder	CodeCode Available	0
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow	Mar 5, 2023	Anomaly DetectionVocal Bursts Intensity Prediction	—Unverified	0
Achieving Counterfactual Fairness for Anomaly Detection	Mar 4, 2023	Anomaly Detectioncounterfactual	CodeCode Available	0

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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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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