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 2701–2710 of 4856 papers

Title	Date	Tasks	Status
CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks	Jul 7, 2024	Anomaly Detection	—Unverified
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection	Aug 20, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Cech Complex Generation with Homotopy Equivalence Framework for Myocardial Infarction Diagnosis using Electrocardiogram Signals	Dec 23, 2024	Anomaly DetectionRhythm	—Unverified
Cellwise and Casewise Robust Covariance in High Dimensions	May 26, 2025	Anomaly Detection	—Unverified
CFOF: A Concentration Free Measure for Anomaly Detection	Jan 14, 2019	Anomaly Detection	—Unverified
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks	Feb 27, 2024	Anomaly DetectionGraph Generation	—Unverified
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization	Apr 18, 2025	Action LocalizationAnomaly Detection	—Unverified
Challenges for Unsupervised Anomaly Detection in Particle Physics	Oct 13, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos	Jun 11, 2019	Anomaly Detection	—Unverified
Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning	Apr 7, 2020	Anomaly DetectionBIG-bench Machine Learning	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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