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 3561–3570 of 4856 papers

Title	Date	Tasks	Status	Hype
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey	Jan 25, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Deep One-Class Classification via Interpolated Gaussian Descriptor	Jan 25, 2021	Anomaly DetectionClassification	CodeCode Available	1
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs	Jan 20, 2021	Anomaly DetectionSSIM	—Unverified	0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch	Jan 18, 2021	Anomaly DetectionTime Series	—Unverified	0
Anomaly Detection Support Using Process Classification	Jan 13, 2021	Anomaly DetectionClassification	—Unverified	0
Multi-Source Anomaly Detection in Distributed IT Systems	Jan 13, 2021	Anomaly Detection	—Unverified	0
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles	Jan 13, 2021	Anomaly DetectionAttribute	—Unverified	0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems	Jan 13, 2021	Anomaly DetectionChange Point Detection	CodeCode Available	0
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators	Jan 12, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder	Jan 11, 2021	Anomaly Detection	—Unverified	0

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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