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 4011–4020 of 4856 papers

Title	Date	Tasks	Status
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection	Oct 2, 2016	Anomaly DetectionEvent Detection	—Unverified
PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure	May 27, 2019	Anomaly Detection	—Unverified
PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection	Dec 17, 2024	3D Anomaly DetectionAnomaly Detection	—Unverified
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour	Apr 29, 2023	Anomaly DetectionIntrusion Detection	—Unverified
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder	Jun 4, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
Polyra Swarms: A Shape-Based Approach to Machine Learning	Jun 16, 2025	Anomaly Detection	—Unverified
Population Anomaly Detection through Deep Gaussianization	May 5, 2018	Anomaly Detection	—Unverified
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1	Mar 6, 2024	Anomaly DetectionEarth Observation	—Unverified
Position: Untrained Machine Learning for Anomaly Detection	Feb 6, 2025	Anomaly DetectionPosition	—Unverified
Positive-Unlabeled Node Classification with Structure-aware Graph Learning	Oct 20, 2023	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