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 1591–1600 of 4856 papers

Title	Date	Tasks	Status	Hype
Eliciting Latent Knowledge from Quirky Language Models	Dec 2, 2023	Anomaly DetectionMath	CodeCode Available	1
Online Anomaly Detection over Live Social Video Streaming	Dec 1, 2023	Anomaly DetectionDecoder	—Unverified	0
Anomaly Detection via Learning-Based Sequential Controlled Sensing	Nov 30, 2023	Anomaly DetectionDecision Making	—Unverified	0
Unsupervised textile defect detection using convolutional neural networks	Nov 30, 2023	Anomaly DetectionDefect Detection	—Unverified	0
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection	Nov 30, 2023	Anomaly DetectionSelf-Supervised Learning	—Unverified	0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection	Nov 29, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available	0
Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit	Nov 28, 2023	Anomaly Detection	—Unverified	0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis	Nov 28, 2023	Anomaly DetectionIntrusion Detection	—Unverified	0
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder	Nov 28, 2023	Anomaly Detection	CodeCode Available	0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach	Nov 27, 2023	Anomaly DetectionOne-Class Classification	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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