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 491–500 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark	Oct 12, 2015	Anomaly DetectionTime Series	CodeCode Available	1	5
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition	Jul 6, 2022	Anomaly DetectionOpen Set Learning	CodeCode Available	1	5
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection	Nov 21, 2022	Anomaly DetectionDecoder	CodeCode Available	1	5
DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision	Jul 28, 2020	Anomaly Detection	CodeCode Available	1	5
Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection	Jul 13, 2021	Anomaly Detection	CodeCode Available	1	5
Detecting Anomalies within Time Series using Local Neural Transformations	Feb 8, 2022	Anomaly DetectionEpidemiology	CodeCode Available	1	5
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots	Sep 20, 2022	Anomaly Detection	CodeCode Available	1	5
A General Framework For Detecting Anomalous Inputs to DNN Classifiers	Jul 29, 2020	Anomaly Detectionimage-classification	CodeCode Available	1	5
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection	Dec 18, 2024	3D Anomaly DetectionAnomaly Detection	CodeCode Available	1	5
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection	Oct 19, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	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