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 431–440 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays	Jun 8, 2022	Anomaly DetectionOne-Class Classification	CodeCode Available	1	5
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning	May 30, 2023	Anomaly DetectionData Augmentation	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
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series	Oct 10, 2020	Anomaly DetectionTime Series	CodeCode Available	1	5
AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme	May 8, 2023	Anomaly DetectionTime Series	CodeCode Available	1	5
Dual-path Frequency Discriminators for Few-shot Anomaly Detection	Mar 7, 2024	Anomaly Detection	CodeCode Available	1	5
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types	Dec 21, 2021	Anomaly DetectionClustering	CodeCode Available	1	5
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks	Aug 28, 2023	Anomaly DetectionGraph Neural Network	CodeCode Available	1	5
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection	Mar 23, 2022	Anomaly DetectionMultiple Instance Learning	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

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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