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 911–920 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Making Reconstruction-based Method Great Again for Video Anomaly Detection	Jan 28, 2023	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1	5
Mixed supervision for surface-defect detection: from weakly to fully supervised learning	Apr 13, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1	5
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation	Jun 18, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1	5
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation	Sep 21, 2022	Anomaly DetectionData Augmentation	CodeCode Available	1	5
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization	Jan 23, 2025	AnatomyAnomaly Detection	CodeCode Available	1	5
Improving Position Encoding of Transformers for Multivariate Time Series Classification	May 26, 2023	Anomaly DetectionPosition	CodeCode Available	1	5
Incorporating Feedback into Tree-based Anomaly Detection	Aug 30, 2017	Anomaly Detection	CodeCode Available	1	5
Unsupervised anomaly detection algorithms on real-world data: how many do we need?	May 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	5
Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction	Apr 5, 2023	Anomaly DetectionDiversity	CodeCode Available	1	5
Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT devices	Jun 28, 2022	Anomaly Detection	CodeCode Available	1	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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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