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 1601–1610 of 4856 papers

Title	Date	Tasks	Status	Score
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD	Apr 29, 2024	Anomaly Detection	CodeCode Available	5
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring	Jun 15, 2022	Anomaly DetectionDimensionality Reduction	CodeCode Available	5
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach	Apr 15, 2024	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	5
Detecting Outliers with Poisson Image Interpolation	Jul 6, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available	5
ENCODE: Encoding NetFlows for Network Anomaly Detection	Jul 8, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
Bump Hunting in Latent Space	Mar 11, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines	Aug 5, 2024	Anomaly Detectionobject-detection	CodeCode Available	5
Efficient GAN-Based Anomaly Detection	Feb 17, 2018	Anomaly Detection	CodeCode Available	5
Building and Interpreting Deep Similarity Models	Mar 11, 2020	Anomaly DetectionClustering	CodeCode Available	5
Anomaly Detection in High Dimensional Data	Aug 12, 2019	Anomaly DetectionFeature Engineering	CodeCode Available	5

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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