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 2201–2210 of 4856 papers

Title	Date	Tasks	Status
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection	May 12, 2023	Anomaly DetectionHuman Detection	—Unverified
Anomaly Detection using Capsule Networks for High-dimensional Datasets	Dec 27, 2021	Anomaly DetectionBinary Classification	—Unverified
A method for incremental discovery of financial event types based on anomaly detection	Feb 16, 2023	Anomaly DetectionDeep Clustering	—Unverified
ConditionNET: Learning Preconditions and Effects for Execution Monitoring	Feb 3, 2025	Anomaly DetectionLanguage Modeling	—Unverified
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos	Feb 4, 2020	Action DetectionAction Recognition	—Unverified
Granular Learning with Deep Generative Models using Highly Contaminated Data	Jan 6, 2020	Anomaly DetectionGeneral Classification	—Unverified
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning	Nov 25, 2024	Anomaly DetectionEEG	—Unverified
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss	Jun 16, 2025	Anomaly Detectionfeature selection	—Unverified
Condition monitoring and anomaly detection in cyber-physical systems	Jan 22, 2023	Anomaly Detection	—Unverified
Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach	Dec 3, 2023	Anomaly 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	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