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 4611–4620 of 4856 papers

Title	Date	Tasks	Status
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice	Sep 13, 2024	Anomaly DetectionBinary Classification	CodeCode Available
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Network	Sep 2, 2019	Anomaly DetectionTime Series	CodeCode Available
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection	Oct 14, 2019	Anomaly Detection	CodeCode Available
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection	Jul 3, 2019	Anomaly DetectionDecoder	CodeCode Available
Optimal Strategies Against Generative Attacks	May 1, 2020	Anomaly Detection	CodeCode Available
FABLE : Fabric Anomaly Detection Automation Process	Jun 16, 2023	Anomaly DetectionDefect Detection	CodeCode Available
Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction	May 1, 2020	Anomaly DetectionClustering	CodeCode Available
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection	May 3, 2024	Anomaly DetectionData Augmentation	CodeCode Available
Extended Isolation Forest	Nov 6, 2018	Anomaly DetectionMulti-Object Tracking	CodeCode Available
Unsupervised Representation Learning by Predicting Random Distances	Dec 22, 2019	Anomaly DetectionClustering	CodeCode Available

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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	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