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 2961–2970 of 4856 papers

Title	Date	Tasks	Status	Hype
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection	Feb 9, 2022	Anomaly DetectionFederated Learning	—Unverified	0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion	Feb 8, 2022	Anomaly DetectionContrastive Learning	—Unverified	0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data	Feb 8, 2022	Anomaly DetectionTime Series	—Unverified	0
Time Series Anomaly Detection by Cumulative Radon Features	Feb 8, 2022	Anomaly DetectionTime Series	CodeCode Available	1
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting	Feb 8, 2022	Anomaly DetectionTime Series	—Unverified	0
Detecting Anomalies within Time Series using Local Neural Transformations	Feb 8, 2022	Anomaly DetectionEpidemiology	CodeCode Available	1
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification	Feb 8, 2022	Anomaly DetectionBenchmarking	CodeCode Available	0
Robust Anomaly Detection for Time-series Data	Feb 6, 2022	Anomaly DetectionTime Series	—Unverified	0
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Robust Audio Anomaly Detection	Feb 3, 2022	Anomaly DetectionTime Series	—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