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 3181–3190 of 4856 papers

Title	Date	Tasks	Status
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring	Mar 19, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection	Mar 19, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection	Mar 17, 2022	Activity PredictionAnomaly Detection	CodeCode Available
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models	Mar 17, 2022	Anomaly DetectionGraph Embedding	—Unverified
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks	Mar 17, 2022	Anomaly DetectionBackdoor Attack	—Unverified
Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning	Mar 15, 2022	Anomaly DetectionSensitivity	—Unverified
A Framework for Verifiable and Auditable Federated Anomaly Detection	Mar 15, 2022	Anomaly DetectionEnsemble Learning	—Unverified
Driving Anomaly Detection Using Conditional Generative Adversarial Network	Mar 15, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified
Practical data monitoring in the internet-services domain	Mar 15, 2022	Anomaly Detection	CodeCode Available
Feature space reduction as data preprocessing for the anomaly detection	Mar 13, 2022	Anomaly Detection	CodeCode Available

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