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 4231–4240 of 4856 papers

Title	Date	Tasks	Status
An Unsupervised Framework for Anomaly Detection in a Water Treatment System.	Dec 16, 2019	Anomaly DetectionDecoder	CodeCode Available
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos	Jan 9, 2018	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection	Apr 4, 2019	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection	Jan 11, 2022	Anomaly DetectionDictionary Learning	CodeCode Available
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models	Mar 9, 2025	Anomaly DetectionMamba	CodeCode Available
Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis	Jan 22, 2024	Anomaly Detection	CodeCode Available
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization	Mar 30, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks	Dec 26, 2022	Anomaly DetectionGraph Neural Network	CodeCode Available
A Novel Multi-Stage Approach for Hierarchical Intrusion Detection	Mar 21, 2023	Anomaly DetectionIntrusion Detection	CodeCode Available
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI	Nov 22, 2022	Anomaly DetectionMetric Learning	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