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 4271–4280 of 4856 papers

Title	Date	Tasks	Status
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy	Oct 27, 2024	Anomaly Detection	CodeCode Available
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection	May 16, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning	Jul 13, 2024	Anomaly DetectionAttribute	CodeCode Available
syslrn: Learning What to Monitor for Efficient Anomaly Detection	Mar 29, 2022	Anomaly Detection	CodeCode Available
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset	Nov 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection	Jan 26, 2025	Anomaly DetectionAutonomous Driving	CodeCode Available
MIXAD: Memory-Induced Explainable Time Series Anomaly Detection	Oct 30, 2024	Anomaly DetectionTime Series	CodeCode Available
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data	Nov 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection	Jul 16, 2024	Anomaly DetectionNovelty Detection	CodeCode Available
Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional Autoencoders	Sep 30, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available

Show:10 25 50

← PrevPage 428 of 486Next →

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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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