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 3511–3520 of 4856 papers

Title	Date	Tasks	Status
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning	Mar 19, 2024	Anomaly DetectionContinual Learning	—Unverified
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection	Aug 13, 2024	Anomaly DetectionFairness	—Unverified
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality	Jul 11, 2023	Anomaly Detection	—Unverified
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency	Mar 9, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks	Dec 7, 2018	Anomaly DetectionDiagnostic	—Unverified
Use of in-the-wild images for anomaly detection in face anti-spoofing	Jun 18, 2020	Anomaly DetectionBinary Classification	—Unverified
usfAD Based Effective Unknown Attack Detection Focused IDS Framework	Mar 17, 2024	Anomaly DetectionIntrusion Detection	—Unverified
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence	Aug 5, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Using a Neural Network to Detect Anomalies given an N-gram Profile	Apr 12, 2021	Anomaly Detection	—Unverified
Using anomaly detection to support classification of fast running (packaging) processes	Jun 6, 2019	Anomaly DetectionGeneral Classification	—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