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 1391–1400 of 4856 papers

Title	Date	Tasks	Status	Score
Few-shot Online Anomaly Detection and Segmentation	Mar 27, 2024	Anomaly Detection	CodeCode Available	5
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification	Feb 8, 2022	Anomaly DetectionBenchmarking	CodeCode Available	5
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series	Aug 5, 2024	Anomaly DetectionAstronomy	CodeCode Available	5
Fence GAN: Towards Better Anomaly Detection	Apr 2, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available	5
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images	Mar 21, 2023	Anomaly DetectionChange Detection	CodeCode Available	5
Combining Machine Learning Models using combo Library	Sep 21, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning	May 6, 2025	Active LearningAnomaly Detection	CodeCode Available	5
Learning Networks from Random Walk-Based Node Similarities	Jan 23, 2018	Anomaly DetectionClustering	CodeCode Available	5
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization	Oct 16, 2024	Anomaly DetectionFederated Learning	CodeCode Available	5
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD	Nov 23, 2022	Anomaly DetectionOutlier Detection	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