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 621–630 of 4856 papers

Title	Date	Tasks	Status	Hype
Dual-path Frequency Discriminators for Few-shot Anomaly Detection	Mar 7, 2024	Anomaly Detection	CodeCode Available	1
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches	Jan 1, 2022	Anomaly DetectionChange Detection	CodeCode Available	1
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders	Jun 23, 2020	Anomaly DetectionMedical Image Analysis	CodeCode Available	1
Anomaly Detection under Distribution Shift	Mar 24, 2023	Anomaly Detection	CodeCode Available	1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions	Jan 2, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	1
Can LLMs Understand Time Series Anomalies?	Oct 7, 2024	Anomaly DetectionTime Series	CodeCode Available	1
Eliciting Latent Knowledge from Quirky Language Models	Dec 2, 2023	Anomaly DetectionMath	CodeCode Available	1
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images	Aug 9, 2020	AnatomyAnomaly Detection	CodeCode Available	1
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters	Aug 19, 2020	Anomaly DetectionFraud Detection	CodeCode Available	1
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset	Sep 30, 2024	Anomaly Detection	CodeCode Available	1

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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