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 4541–4550 of 4856 papers

Title	Date	Tasks	Status
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders	Aug 30, 2018	Anomaly Detection	CodeCode Available
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images	Apr 12, 2018	AnatomyAnomaly Detection	CodeCode Available
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	Jan 1, 2018	Anomaly DetectionDensity Estimation	CodeCode Available
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks	Mar 6, 2024	Anomaly DetectionBenchmarking	CodeCode Available
Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM	Aug 30, 2022	Anomaly DetectionSSIM	CodeCode Available
Feature space reduction as data preprocessing for the anomaly detection	Mar 13, 2022	Anomaly Detection	CodeCode Available
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection	Jul 5, 2024	Anomaly DetectionEdge-computing	CodeCode Available
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series	Dec 19, 2018	Anomaly DetectionTime Series	CodeCode Available
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams	Jun 6, 2018	Anomaly DetectionDeep Learning	CodeCode Available
Deep Anomaly Detection with Deviation Networks	Nov 19, 2019	Anomaly DetectionCyber Attack Detection	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