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 231–240 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Are we certain it's anomalous?	Nov 16, 2022	Anomaly DetectionTime Series	CodeCode Available	1	5
A SAM-guided Two-stream Lightweight Model for Anomaly Detection	Feb 29, 2024	Anomaly DetectionDecoder	CodeCode Available	1	5
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection	Feb 18, 2020	Anomaly DetectionDecoder	CodeCode Available	1	5
Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation	Dec 19, 2021	Anomaly DetectionKnowledge Distillation	CodeCode Available	1	5
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video	Aug 27, 2020	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1	5
Deep Learning for Anomaly Detection in Log Data: A Survey	Jul 8, 2022	Anomaly DetectionDeep Learning	CodeCode Available	1	5
Anomaly Detection using Score-based Perturbation Resilience	Jan 1, 2023	Anomaly DetectionDenoising	CodeCode Available	1	5
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	Jun 14, 2021	Anomaly DetectionDeep Learning	CodeCode Available	1	5
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data	Dec 25, 2021	Anomaly Detectioncontinual anomaly detection	CodeCode Available	1	5
Attention-based residual autoencoder for video anomaly detection	May 25, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	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