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 1581–1590 of 4856 papers

Title	Date	Tasks	Status
Anomaly Subsequence Detection with Dynamic Local Density for Time Series	Jun 28, 2019	Anomaly DetectionDensity Estimation	—Unverified
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle	Dec 28, 2020	Anomaly DetectionSynthetic Data Generation	—Unverified
Detecting Anomalous User Behavior in Remote Patient Monitoring	Jun 22, 2021	Anomaly Detection	—Unverified
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis	Jan 7, 2022	Anomaly Detection	—Unverified
Detecting Attacks on IoT Devices using Featureless 1D-CNN	Sep 9, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection	Nov 4, 2020	Anomaly DetectionData Augmentation	—Unverified
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data	Jun 10, 2015	Anomaly DetectionGroup Anomaly Detection	—Unverified
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing	Apr 21, 2024	Anomaly Detection	—Unverified
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions	Jan 14, 2025	Anomaly DetectionClustering	—Unverified
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network	Dec 28, 2024	Anomaly DetectionBayesian Inference	—Unverified

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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