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 4171–4180 of 4856 papers

Title	Date	Tasks	Status
Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks	Oct 30, 2022	Anomaly DetectionBenchmarking	CodeCode Available
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection	May 30, 2024	Anomaly DetectionTime Series	CodeCode Available
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection	May 25, 2025	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
Context Encoding Chest X-rays	Dec 3, 2018	Anomaly DetectionImage Inpainting	CodeCode Available
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection	Jun 6, 2023	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series	Dec 21, 2022	Anomaly DetectionTime Series	CodeCode Available
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach	Oct 22, 2019	Anomaly DetectionMobile Security	CodeCode Available
MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption	May 31, 2022	Anomaly DetectionDiversity	CodeCode Available
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks	Jan 15, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Subtractive Aggregation for Attributed Network Anomaly Detection	Oct 26, 2021	Anomaly DetectionGraph Neural Network	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