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 861–870 of 4856 papers

Title	Date	Tasks	Status	Hype
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection	May 28, 2020	Anomaly DetectionTransfer Learning	CodeCode Available	1
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring	May 20, 2020	Anomaly DetectionBayesian Optimization	CodeCode Available	1
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs	May 15, 2020	Anomaly DetectionGraph Neural Network	CodeCode Available	1
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization	May 12, 2020	Anomaly DetectionMetric Learning	CodeCode Available	1
Learning Generalized Spoof Cues for Face Anti-spoofing	May 8, 2020	Anomaly DetectionDiversity	CodeCode Available	1
Classification-Based Anomaly Detection for General Data	May 5, 2020	Anomaly DetectionClassification	CodeCode Available	1
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection	May 5, 2020	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Sub-Image Anomaly Detection with Deep Pyramid Correspondences	May 5, 2020	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations	May 4, 2020	Anomaly DetectionDensity Estimation	CodeCode Available	1
RaPP: Novelty Detection with Reconstruction along Projection Pathway	May 1, 2020	Anomaly DetectionNovelty Detection	CodeCode Available	1

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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