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 1491–1500 of 4856 papers

Title	Date	Tasks	Status	Score
Context Encoding Chest X-rays	Dec 3, 2018	Anomaly DetectionImage Inpainting	CodeCode Available	5
FABLE : Fabric Anomaly Detection Automation Process	Jun 16, 2023	Anomaly DetectionDefect Detection	CodeCode Available	5
Explaining Anomalies in Groups with Characterizing Subspace Rules	Aug 20, 2017	Anomaly DetectionDescriptive	CodeCode Available	5
Anomaly detection in the dynamics of web and social networks	Jan 22, 2019	Anomaly DetectionTime Series	CodeCode Available	5
Algorithmic Frameworks for the Detection of High Density Anomalies	Oct 9, 2020	Anomaly DetectionVocal Bursts Intensity Prediction	CodeCode Available	5
Deep Anomaly Detection for Generalized Face Anti-Spoofing	Apr 17, 2019	Anomaly DetectionFace Anti-Spoofing	CodeCode Available	5
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series	Apr 15, 2024	Anomaly DetectionCausal Discovery	CodeCode Available	5
Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection	Jun 17, 2025	Anomaly Detection	CodeCode Available	5
Exploring Deep Anomaly Detection Methods Based on Capsule Net	Jul 15, 2019	Anomaly DetectionPrediction	CodeCode Available	5
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection	Oct 14, 2019	Anomaly Detection	CodeCode Available	5

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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