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

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Showing 1211–1220 of 4856 papers

Title	Date	Tasks	Status	Score
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly Detection	Oct 27, 2024	Anomaly DetectionContrastive Learning	CodeCode Available	5
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient	Dec 11, 2024	Anomaly DetectionOutlier Detection	CodeCode Available	5
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection	Mar 18, 2019	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	5
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection	Aug 29, 2023	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	5
Good Practices and A Strong Baseline for Traffic Anomaly Detection	May 9, 2021	Anomaly DetectionManagement	CodeCode Available	5
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning	Mar 6, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
An anomaly detection approach for backdoored neural networks: face recognition as a case study	Aug 22, 2022	Anomaly DetectionFace Recognition	CodeCode Available	5
Generator Based Inference (GBI)	May 30, 2025	Anomaly Detectionparameter estimation	CodeCode Available	5
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection	Dec 2, 2019	Anomaly Detection	CodeCode Available	5
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning	May 6, 2025	Active LearningAnomaly Detection	CodeCode Available	5

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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