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 2001–2010 of 4856 papers

Title	Date	Tasks	Status	Hype
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection	Jun 17, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	2
MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection	Jun 16, 2023	Anomaly DetectionKnowledge Distillation	CodeCode Available	0
FABLE : Fabric Anomaly Detection Automation Process	Jun 16, 2023	Anomaly DetectionDefect Detection	CodeCode Available	0
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects	Jun 16, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	2
Unsupervised Anomaly Detection via Nonlinear Manifold Learning	Jun 15, 2023	Anomaly DetectionNovelty Detection	—Unverified	0
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data	Jun 15, 2023	Anomaly Detection	—Unverified	0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models	Jun 15, 2023	Anomaly DetectionInstance Segmentation	—Unverified	0
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection	Jun 15, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	2
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection	Jun 14, 2023	Anomaly DetectionData Augmentation	—Unverified	0
Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learning	Jun 14, 2023	Anomaly DetectionDecision Making	—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