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 1441–1450 of 4856 papers

Title	Date	Tasks	Status
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )	Jun 1, 2022	Anomaly DetectionPrompt Engineering	—Unverified
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques	Aug 6, 2023	Anomaly DetectionTime Series	—Unverified
An optimization method for out-of-distribution anomaly detection models	Feb 2, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning	Oct 7, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction	Nov 14, 2024	Anomaly DetectionClustering	—Unverified
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection	Dec 9, 2019	Anomaly DetectionIncremental Learning	—Unverified
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies	Apr 20, 2023	Anomaly DetectionDenoising	—Unverified
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization	Sep 25, 2020	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata	Oct 29, 2020	Anomaly Detection	—Unverified
Deep Anomaly Detection on Tennessee Eastman Process Data	Mar 10, 2023	Anomaly DetectionChemical Process	—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	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