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 4151–4160 of 4856 papers

Title	Date	Tasks	Status
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction	Apr 29, 2024	Anomaly DetectionTrajectory Prediction	CodeCode Available
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach	Sep 30, 2024	Anomaly Detection	CodeCode Available
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process	Dec 20, 2018	Anomaly Detection	CodeCode Available
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection	Aug 23, 2024	Anomaly DetectionContrastive Learning	CodeCode Available
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection	May 20, 2021	Anomaly DetectionClustering	CodeCode Available
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer	May 13, 2025	Anomaly DetectionEpidemiology	CodeCode Available
Machine learning-based identification of Gaia astrometric exoplanet orbits	Apr 14, 2024	Anomaly DetectionFeature Importance	CodeCode Available
CloudShield: Real-time Anomaly Detection in the Cloud	Aug 20, 2021	Anomaly DetectionCloud Computing	CodeCode Available
Distillation-based fabric anomaly detection	Jan 4, 2024	Anomaly DetectionDefect Detection	CodeCode Available
Disentangling Tabular Data Towards Better One-Class Anomaly Detection	Nov 12, 2024	Anomaly DetectionDisentanglement	CodeCode Available

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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