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 1271–1280 of 4856 papers

Title	Date	Tasks	Status
ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment	Apr 24, 2024	Anomaly Detection	—Unverified
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization	Sep 25, 2020	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
A convolutional neural network of low complexity for tumor anomaly detection	Jan 24, 2023	Anomaly DetectionClassification	—Unverified
An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances	Jan 6, 2022	Anomaly Detection	—Unverified
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes	Sep 3, 2016	Anomaly DetectionOutlier Detection	—Unverified
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches	Nov 4, 2024	Anomaly DetectionCyber Attack Detection	—Unverified
Are vision language models robust to uncertain inputs?	May 17, 2025	Anomaly DetectionMorphology classification	—Unverified
An Incremental Clustering Method for Anomaly Detection in Flight Data	May 20, 2020	Anomaly DetectionClustering	—Unverified
A review on outlier/anomaly detection in time series data	Feb 11, 2020	Anomaly DetectionOutlier Detection	—Unverified
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection	Jan 22, 2024	Anomaly DetectionComputational Efficiency	—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