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 1301–1310 of 4856 papers

Title	Date	Tasks	Status
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset	Aug 25, 2022	Anomaly DetectionECG Classification	—Unverified
A Representation Learning Approach to Feature Drift Detection in Wireless Networks	May 15, 2025	Anomaly DetectionDrift Detection	—Unverified
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series	May 6, 2024	Anomaly DetectionTime Series	—Unverified
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving	Feb 23, 2025	Anomaly DetectionAutonomous Driving	—Unverified
Are Large Language Models Useful for Time Series Data Analysis?	Dec 16, 2024	Anomaly DetectionDecision Making	—Unverified
A new Video Synopsis Based Approach Using Stereo Camera	Jun 23, 2021	Anomaly DetectionVideo Synopsis	—Unverified
Advances on the classification of radio image cubes	May 5, 2023	Anomaly DetectionAstronomy	—Unverified
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption	Sep 11, 2024	Anomaly DetectionComputational Efficiency	—Unverified
A Recover-then-Discriminate Framework for Robust Anomaly Detection	Jun 7, 2024	Anomaly Detection	—Unverified
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training	Mar 24, 2016	Anomaly DetectionDimensionality Reduction	—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