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 4491–4500 of 4856 papers

Title	Date	Tasks	Status
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection	Jun 21, 2024	Anomaly DetectionEvent Detection	CodeCode Available
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text	Jul 1, 2019	Anomaly DetectionContextual Anomaly Detection	CodeCode Available
fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library	Jun 10, 2024	Anomaly DetectionCPU	CodeCode Available
Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection	Mar 30, 2025	Anomaly DetectionDiversity	CodeCode Available
What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon	Aug 10, 2024	Anomaly DetectionAutonomous Driving	CodeCode Available
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning	Dec 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
When and How Does In-Distribution Label Help Out-of-Distribution Detection?	May 28, 2024	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available
Deep Learning for Anomaly Detection: A Survey	Jan 10, 2019	Anomaly DetectionDeep Learning	CodeCode Available
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrograms	Jul 3, 2023	Anomaly DetectionAstronomy	CodeCode Available
Deep learning achieves perfect anomaly detection on 108,308 retinal images including unlearned diseases	Jan 13, 2020	Anomaly Detection	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