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 801–810 of 4856 papers

Title	Date	Tasks	Status	Hype
Learning and Evaluating Representations for Deep One-class Classification	Nov 4, 2020	Anomaly DetectionClassification	CodeCode Available	1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models	Oct 27, 2020	Anomaly DetectionChange Point Detection	CodeCode Available	1
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers	Oct 21, 2020	Anomaly DetectionKeyword Spotting	CodeCode Available	1
Reconstruction by Inpainting for Visual Anomaly Detection	Oct 17, 2020	Anomaly Detection	CodeCode Available	1
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation	Oct 12, 2020	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate	Oct 10, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series	Oct 10, 2020	Anomaly DetectionTime Series	CodeCode Available	1
Driver Anomaly Detection: A Dataset and Contrastive Learning Approach	Sep 30, 2020	Anomaly DetectionContrastive Learning	CodeCode Available	1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress	Sep 29, 2020	Anomaly DetectionTime Series	CodeCode Available	1
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection	Sep 24, 2020	Anomaly DetectionGroup Anomaly Detection	CodeCode Available	1

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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