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 3251–3260 of 4856 papers

Title	Date	Tasks	Status
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data	Feb 8, 2022	Anomaly DetectionTime Series	—Unverified
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting	Feb 8, 2022	Anomaly DetectionTime Series	—Unverified
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification	Feb 8, 2022	Anomaly DetectionBenchmarking	CodeCode Available
Robust Anomaly Detection for Time-series Data	Feb 6, 2022	Anomaly DetectionTime Series	—Unverified
Robust Audio Anomaly Detection	Feb 3, 2022	Anomaly DetectionTime Series	—Unverified
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection	Feb 2, 2022	Anomaly DetectionIntrusion Detection	—Unverified
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection	Feb 1, 2022	Anomaly DetectionTime Series Analysis	—Unverified
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction	Jan 31, 2022	AnatomyAnomaly Detection	—Unverified
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks	Jan 31, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors	Jan 31, 2022	Anomaly DetectionBinary Classification	—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	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