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 281–290 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Deep and Confident Prediction for Time Series at Uber	Sep 6, 2017	Anomaly DetectionPrediction	CodeCode Available	1	5
Deep Anomaly Detection with Outlier Exposure	Dec 11, 2018	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	1	5
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection	Feb 15, 2022	Anomaly DetectionMissing Values	CodeCode Available	1	5
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders	Jun 23, 2020	Anomaly DetectionMedical Image Analysis	CodeCode Available	1	5
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection	Jun 9, 2021	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1	5
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series	Apr 17, 2024	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
DAGAD: Data Augmentation for Graph Anomaly Detection	Oct 18, 2022	Anomaly DetectionData Augmentation	CodeCode Available	1	5
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers	Apr 12, 2021	Anomaly Detection	CodeCode Available	1	5
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning	Mar 17, 2024	Anomaly Detection	CodeCode Available	1	5
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization	Apr 8, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1	5

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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