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 981–990 of 4856 papers

Title	Date	Tasks	Status
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning	Oct 21, 2020	Anomaly DetectionDeep Learning	—Unverified
Automated Model Selection for Time-Series Anomaly Detection	Aug 25, 2020	Anomaly Detectionmodel	—Unverified
Anomaly Detection for an E-commerce Pricing System	Feb 25, 2019	Anomaly DetectionModel Selection	—Unverified
A Framework of Sparse Online Learning and Its Applications	Jul 25, 2015	Anomaly DetectionClassification	—Unverified
Anomaly Detection for Industrial Big Data	Apr 9, 2018	Anomaly DetectionTime Series	—Unverified
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency	Apr 8, 2022	Anomaly DetectionOptical Flow Estimation	—Unverified
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support	Sep 14, 2018	Anomaly DetectionRetrieval	—Unverified
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees	Apr 14, 2021	Anomaly Detection	—Unverified
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data	May 13, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection	Nov 27, 2021	Anomaly DetectionDecoder	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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