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 481–490 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data	Sep 15, 2020	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1	5
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis	Feb 20, 2023	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1	5
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection	Nov 14, 2022	Anomaly Detection	CodeCode Available	1	5
Deep Sets	Mar 10, 2017	Anomaly DetectionOutlier Detection	CodeCode Available	1	5
Deep Structured Energy Based Models for Anomaly Detection	May 25, 2016	Anomaly Detection	CodeCode Available	1	5
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies	Mar 11, 2022	Anomaly DetectionInformation Retrieval	CodeCode Available	1	5
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach	Aug 2, 2023	Anomaly DetectionDeep Learning	CodeCode Available	1	5
Delving into CLIP latent space for Video Anomaly Recognition	Oct 4, 2023	Anomaly DetectionMultiple Instance Learning	CodeCode Available	1	5
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters	Aug 19, 2020	Anomaly DetectionFraud Detection	CodeCode Available	1	5
Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning	Jun 29, 2021	Anomaly DetectionCloud Computing	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