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 3891–3900 of 4856 papers

Title	Date	Tasks	Status
Simulation-Assisted Decorrelation for Resonant Anomaly Detection	Sep 4, 2020	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks	Aug 31, 2020	Anomaly DetectionSelf-Supervised Learning	—Unverified
Anomaly Detection by Recombining Gated Unsupervised Experts	Aug 31, 2020	Anomaly DetectionMixture-of-Experts	CodeCode Available
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform	Aug 31, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection	Aug 31, 2020	Anomaly Detection	CodeCode Available
Puzzle-AE: Novelty Detection in Images through Solving Puzzles	Aug 29, 2020	Anomaly DetectionNovelty Detection	CodeCode Available
GAN-based Anomaly Detection in Imbalance Problems	Aug 28, 2020	Anomaly DetectionDefect Detection	—Unverified
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels	Aug 27, 2020	Anomaly DetectionClustering	—Unverified
The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets	Aug 27, 2020	Anomaly Detection	CodeCode Available
Training robust anomaly detection using ML-Enhanced simulations	Aug 27, 2020	Anomaly Detection	—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	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