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 451–460 of 4856 papers

Title	Date	Tasks	Status
Using Causality for Enhanced Prediction of Web Traffic Time Series	Feb 2, 2025	Anomaly DetectionFraud Detection	—Unverified
Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework	Feb 1, 2025	Anomaly DetectionDiversity	—Unverified
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes	Jan 31, 2025	Anomaly DetectionDataset Generation	—Unverified
An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus	Jan 31, 2025	Anomaly DetectionAutonomous Vehicles	—Unverified
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments	Jan 31, 2025	Anomaly Detection	CodeCode Available
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)	Jan 30, 2025	Anomaly DetectionCausal Inference	—Unverified
Battery State of Health Estimation Using LLM Framework	Jan 30, 2025	Anomaly Detection	—Unverified
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection	Jan 30, 2025	Anomaly DetectionTime Series	CodeCode Available
KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units	Jan 29, 2025	Anomaly DetectionTime Series	—Unverified
Detecting Anomalies Using Rotated Isolation Forest	Jan 29, 2025	Anomaly DetectionUnsupervised 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