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 681–690 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Are we certain it's anomalous?	Nov 16, 2022	Anomaly DetectionTime Series	CodeCode Available	1	5
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection	Dec 5, 2023	Anomaly DetectionTime Series	CodeCode Available	1	5
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications	Sep 23, 2021	Anomaly Detection	CodeCode Available	1	5
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection	Aug 23, 2021	Anomaly DetectionAttribute	CodeCode Available	1	5
Deep Anomaly Detection on Attributed Networks	May 2, 2019	Anomaly DetectionAttribute	CodeCode Available	1	5
Deep and Confident Prediction for Time Series at Uber	Sep 6, 2017	Anomaly DetectionPrediction	CodeCode Available	1	5
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges	Jan 25, 2025	Anomaly DetectionDiversity	CodeCode Available	1	5
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms	Apr 21, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1	5
Generator Versus Segmentor: Pseudo-healthy Synthesis	Sep 12, 2020	Anomaly DetectionGenerative Adversarial Network	CodeCode Available	1	5
Anomaly Detection in Emails using Machine Learning and Header Information	Mar 19, 2022	Anomaly DetectionBIG-bench Machine Learning	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	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