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 1061–1070 of 4856 papers

Title	Date	Tasks	Status	Score
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting	Mar 31, 2025	Anomaly DetectionMultivariate Time Series Forecasting	CodeCode Available	5
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices	May 30, 2025	Anomaly Detection	CodeCode Available	5
Improved AutoEncoder with LSTM module and KL divergence	Apr 30, 2024	Anomaly Detection	CodeCode Available	5
Improved Anomaly Detection through Conditional Latent Space VAE Ensembles	Oct 16, 2024	Anomaly Detection	CodeCode Available	5
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection	May 14, 2021	Anomaly Detectionvalid	CodeCode Available	5
Improved Anomaly Detection by Using the Attention-Based Isolation Forest	Oct 5, 2022	Anomaly Detection	CodeCode Available	5
Implementing Lightweight Intrusion Detection System on Resource Constrained Devices	Oct 28, 2024	Anomaly DetectionIntrusion Detection	CodeCode Available	5
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems	May 20, 2022	Anomaly DetectionTime Series	CodeCode Available	5
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning	Jul 13, 2024	Anomaly DetectionAttribute	CodeCode Available	5
Improving Novelty Detection using the Reconstructions of Nearest Neighbours	Nov 11, 2021	Anomaly DetectionData Augmentation	CodeCode Available	5

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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