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 1011–1020 of 4856 papers

Title	Date	Tasks	Status	Score
Is AUC the best measure for practical comparison of anomaly detectors?	May 8, 2023	Anomaly Detection	CodeCode Available	5
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain	Jun 12, 2025	Anomaly Detection	CodeCode Available	5
Isconna: Streaming Anomaly Detection with Frequency and Patterns	Apr 4, 2021	Anomaly Detection	CodeCode Available	5
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection	Mar 16, 2025	Anomaly DetectionModel Selection	CodeCode Available	5
Invertible Neural Networks for Graph Prediction	Jun 2, 2022	Anomaly DetectionGraph Neural Network	CodeCode Available	5
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder	Jan 6, 2017	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	5
Inverting Adversarially Robust Networks for Image Synthesis	Jun 13, 2021	Anomaly DetectionDeep Feature Inversion	CodeCode Available	5
Anomaly Detection and Prototype Selection Using Polyhedron Curvature	Apr 5, 2020	Anomaly DetectionDenoising	CodeCode Available	5
Attribute Restoration Framework for Anomaly Detection	Nov 25, 2019	Anomaly DetectionAttribute	CodeCode Available	5
Interruptions detection in video conferences	Feb 25, 2023	Anomaly Detection	CodeCode Available	5

Show:10 25 50

← PrevPage 102 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	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