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 1191–1200 of 4856 papers

Title	Date	Tasks	Status
Activation Learning by Local Competitions	Sep 26, 2022	Adversarial AttackAnomaly Detection	—Unverified
Curved Geometric Networks for Visual Anomaly Recognition	Aug 2, 2022	Anomaly DetectionAnomaly Segmentation	—Unverified
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition	Jul 15, 2023	Anomaly DetectionKeyword Spotting	—Unverified
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation	Jul 13, 2024	Anomaly DetectionClustering	—Unverified
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective	Nov 19, 2016	Anomaly DetectionFraud Detection	—Unverified
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model	Sep 16, 2020	Anomaly DetectionFraud Detection	—Unverified
A Survey of Anomaly Detection in In-Vehicle Networks	Sep 11, 2024	Anomaly DetectionSurvey	—Unverified
A Survey of Anomaly Detection in Cyber-Physical Systems	Feb 18, 2025	Anomaly DetectionSurvey	—Unverified
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings	May 3, 2018	Anomaly DetectionChange Detection	—Unverified
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks	Apr 12, 2019	Anomaly DetectionData Poisoning	—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	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