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 1451–1460 of 4856 papers

Title	Date	Tasks	Status	Score
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects	Dec 2, 2021	Anomaly Detection	CodeCode Available	5
Achieving Counterfactual Fairness for Anomaly Detection	Mar 4, 2023	Anomaly Detectioncounterfactual	CodeCode Available	5
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP	Dec 5, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	5
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence	Aug 17, 2019	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	5
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection	Jun 29, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available	5
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks	Jan 30, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available	5
A Metric Learning Approach to Anomaly Detection in Video Games	May 20, 2020	Anomaly DetectionAtari Games	CodeCode Available	5
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection	Oct 14, 2019	Anomaly Detection	CodeCode Available	5
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders	Aug 30, 2018	Anomaly Detection	CodeCode Available	5
FABLE : Fabric Anomaly Detection Automation Process	Jun 16, 2023	Anomaly DetectionDefect 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