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 1611–1620 of 4856 papers

Title	Date	Tasks	Status
A review of neural network algorithms and their applications in supercritical extraction	Oct 31, 2020	Anomaly DetectionChemical Process	—Unverified
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction	Dec 21, 2024	Anomaly DetectionContrastive Learning	—Unverified
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction	Feb 5, 2023	Anomaly DetectionLoad Forecasting	—Unverified
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts	May 7, 2025	Anomaly DetectionGrammatical Error Detection	—Unverified
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach	Oct 26, 2023	Anomaly DetectionGenerative Adversarial Network	—Unverified
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning	Jun 18, 2018	Anomaly DetectionDeep Learning	—Unverified
Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code	Apr 26, 2010	Anomaly Detection	—Unverified
Detection and Statistical Modeling of Birth-Death Anomaly	Jun 27, 2019	Anomaly Detection	—Unverified
AAD: Adaptive Anomaly Detection through traffic surveillance videos	Aug 29, 2018	Action DetectionActivity Detection	—Unverified
EAPCR: A Universal Feature Extractor for Scientific Data without Explicit Feature Relation Patterns	Nov 12, 2024	Anomaly DetectionDeep Learning	—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