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 2821–2830 of 4856 papers

Title	Date	Tasks	Status
Proximally Sensitive Error for Anomaly Detection and Feature Learning	Jun 1, 2022	Anomaly Detection	—Unverified
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images	Oct 5, 2021	Anomaly DetectionImage Reconstruction	—Unverified
PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data	Nov 22, 2020	Anomaly DetectionMulti-Label Classification	—Unverified
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies	Mar 19, 2023	Anomaly DetectionOne-Class Classification	—Unverified
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing	Dec 5, 2023	Anomaly Detectionclass-incremental learning	—Unverified
PSL is Dead. Long Live PSL	May 27, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision	Jul 9, 2024	Anomaly DetectionBinary Classification	—Unverified
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model	May 24, 2022	Anomaly DetectionDeep Learning	—Unverified
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning	Jan 25, 2023	Anomaly Detection	—Unverified
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies	Jan 16, 2024	Anomaly Detectioncounterfactual	—Unverified

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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