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 2831–2840 of 4856 papers

Title	Date	Tasks	Status	Hype
AI for human assessment: What do professional assessors need?	Apr 18, 2022	Anomaly DetectionDecision Making	—Unverified	0
AFSC: Adaptive Fourier Space Compression for Anomaly Detection	Apr 17, 2022	Anomaly DetectionData Augmentation	—Unverified	0
Anomalous Sound Detection Based on Machine Activity Detection	Apr 15, 2022	Action DetectionActivity Detection	—Unverified	0
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data	Apr 14, 2022	Anomaly DetectionTime Series	CodeCode Available	1
Self-Supervised Losses for One-Class Textual Anomaly Detection	Apr 12, 2022	Anomaly Detection	—Unverified	0
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization	Apr 12, 2022	Anomaly DetectionChange Detection	—Unverified	0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data	Apr 12, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector	Apr 8, 2022	Anomaly DetectionIntrusion Detection	—Unverified	0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency	Apr 8, 2022	Anomaly DetectionOptical Flow Estimation	—Unverified	0
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems	Apr 7, 2022	Anomaly DetectionBIG-bench Machine Learning	—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