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 1451–1460 of 4856 papers

Title	Date	Tasks	Status
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition	Feb 24, 2021	Anomaly DetectionGeneral Classification	—Unverified
An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus	Jan 31, 2025	Anomaly DetectionAutonomous Vehicles	—Unverified
Detection of Abnormal Behavior with Self-Supervised Gaze Estimation	Jul 14, 2021	Anomaly DetectionGaze Estimation	—Unverified
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions	Oct 25, 2022	Anomaly DetectionBenchmarking	—Unverified
Deep Anomaly Detection in Text	Dec 14, 2023	Anomaly DetectionRepresentation Learning	—Unverified
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach	Jul 19, 2020	Anomaly DetectionFederated Learning	—Unverified
Deep End-to-end Unsupervised Anomaly Detection	Sep 25, 2019	Anomaly DetectionClustering	—Unverified
Deep evolving semi-supervised anomaly detection	Dec 1, 2024	Anomaly DetectionContinual Learning	—Unverified
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement	Nov 8, 2021	Anomaly Detection	—Unverified
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems	May 3, 2024	Anomaly DetectionDecoder	—Unverified

Show:10 25 50

← PrevPage 146 of 486Next →

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