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 4461–4470 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection using Autoencoders in High Performance Computing Systems	Nov 13, 2018	Anomaly DetectionVocal Bursts Intensity Prediction	CodeCode Available
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation	Apr 5, 2019	Anomaly DetectionAutonomous Driving	CodeCode Available
SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data Analysis	Sep 4, 2024	Anomaly DetectionIntrusion Detection	CodeCode Available
Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences	Jul 4, 2024	Anomaly Detection	CodeCode Available
The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets	Aug 27, 2020	Anomaly Detection	CodeCode Available
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection	Sep 20, 2021	Anomaly DetectionLesion Detection	CodeCode Available
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees	Jan 1, 2021	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection	Jan 30, 2025	Anomaly DetectionTime Series	CodeCode Available
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data	May 29, 2024	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
NLP-Based .NET CLR Event Logs Analyzer	Feb 6, 2025	Anomaly Detection	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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