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 2771–2780 of 4856 papers

Title	Date	Tasks	Status
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts	May 12, 2020	Anomaly DetectionContour Detection	—Unverified
Computing Graph Descriptors on Edge Streams	Sep 2, 2021	Anomaly DetectionClassification	—Unverified
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots	Oct 21, 2023	Anomaly DetectionManagement	—Unverified
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets	Jul 27, 2022	Anomaly Detection	—Unverified
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model	Jan 15, 2022	Anomaly Detection	—Unverified
Condition monitoring and anomaly detection in cyber-physical systems	Jan 22, 2023	Anomaly Detection	—Unverified
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss	Jun 16, 2025	Anomaly Detectionfeature selection	—Unverified
ConditionNET: Learning Preconditions and Effects for Execution Monitoring	Feb 3, 2025	Anomaly DetectionLanguage Modeling	—Unverified
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection	May 12, 2023	Anomaly DetectionHuman Detection	—Unverified
Conformalized density- and distance-based anomaly detection in time-series data	Aug 16, 2016	Anomaly DetectionIntrusion Detection	—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