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 721–730 of 4856 papers

Title	Date	Tasks	Status	Hype
Learning Latent Space Energy-Based Prior Model	Jun 15, 2020	Anomaly Detectionmodel	CodeCode Available	1
Learning Memory-guided Normality for Anomaly Detection	Mar 30, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots	Sep 20, 2022	Anomaly Detection	CodeCode Available	1
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data	Sep 19, 2023	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Learning Neural Set Functions Under the Optimal Subset Oracle	Mar 3, 2022	Anomaly DetectionDrug Discovery	CodeCode Available	1
Anomaly Detection in Dynamic Graphs via Transformer	Jun 18, 2021	Anomaly Detection	CodeCode Available	1
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization	Aug 30, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges	Jan 25, 2025	Anomaly DetectionDiversity	CodeCode Available	1
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection	Jul 5, 2022	Anomaly DetectionEvent Detection	CodeCode Available	1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection	Dec 19, 2024	Anomaly DetectionEEG	CodeCode Available	1

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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