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 3091–3100 of 4856 papers

Title	Date	Tasks	Status
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning	Nov 13, 2021	Anomaly DetectionFederated Learning	—Unverified
Variation and generality in encoding of syntactic anomaly information in sentence embeddings	Nov 12, 2021	Anomaly DetectionSentence	—Unverified
Through-Foliage Tracking with Airborne Optical Sectioning	Nov 12, 2021	Anomaly Detection	—Unverified
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts	Nov 12, 2021	Anomaly DetectionDisentanglement	—Unverified
Improving Novelty Detection using the Reconstructions of Nearest Neighbours	Nov 11, 2021	Anomaly DetectionData Augmentation	CodeCode Available
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series	Nov 11, 2021	Anomaly DetectionChange Detection	—Unverified
Online-compatible Unsupervised Non-resonant Anomaly Detection	Nov 11, 2021	Anomaly DetectionSensitivity	CodeCode Available
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security	Nov 10, 2021	Anomaly DetectionState Estimation	—Unverified
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement	Nov 8, 2021	Anomaly Detection	—Unverified
A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves	Nov 5, 2021	Anomaly DetectionColorization	—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