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 2351–2360 of 4856 papers

Title	Date	Tasks	Status
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network	Nov 26, 2024	Anomaly DetectionGraph Neural Network	—Unverified
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis	Jun 30, 2022	Anomaly Detection	—Unverified
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection	Jan 9, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art	Apr 1, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Hypergraph-based multi-scale spatio-temporal graph convolution network for Time-Series anomaly detection	Oct 29, 2024	Anomaly DetectionGraph structure learning	—Unverified
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection	May 17, 2025	Anomaly DetectionContinual Learning	—Unverified
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies	Mar 11, 2024	Anomaly DetectionData Compression	—Unverified
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection	May 17, 2025	Anomaly DetectionContinual Learning	—Unverified
Group Anomaly Detection using Flexible Genre Models	Dec 1, 2011	Anomaly DetectionGroup Anomaly Detection	—Unverified
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve	Aug 30, 2024	Anomaly DetectionGraph Neural Network	—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