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 1221–1230 of 4856 papers

Title	Date	Tasks	Status
Data Drift Monitoring for Log Anomaly Detection Pipelines	Oct 17, 2023	Anomaly DetectionDrift Detection	—Unverified
Assessing Cyclostationary Malware Detection via Feature Selection and Classification	Aug 29, 2023	Anomaly DetectionClassification	—Unverified
A spectral-spatial fusion anomaly detection method for hyperspectral imagery	Feb 24, 2022	Anomaly Detection	—Unverified
Anomalous Example Detection in Deep Learning: A Survey	Mar 16, 2020	Anomaly DetectionDeep Learning	—Unverified
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction	Jun 16, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification	Feb 7, 2018	Anomaly DetectionBenchmarking	—Unverified
Anomalous Client Detection in Federated Learning	Nov 3, 2024	Anomaly DetectionEdge-computing	—Unverified
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning	Sep 21, 2021	Anomaly DetectionAtari Games	—Unverified
Anomalous Change Point Detection Using Probabilistic Predictive Coding	May 24, 2024	Anomaly DetectionChange Point Detection	—Unverified
Sequential Adversarial Anomaly Detection for One-Class Event Data	Oct 21, 2019	Anomaly DetectionPoint Processes	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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