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 271–280 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series	Apr 17, 2024	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection	Jun 9, 2021	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1	5
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models	May 13, 2024	Anomaly DetectionEdge Detection	CodeCode Available	1	5
ADGym: Design Choices for Deep Anomaly Detection	Sep 27, 2023	Anomaly DetectionCloud Computing	CodeCode Available	1	5
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning	Mar 17, 2024	Anomaly Detection	CodeCode Available	1	5
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment	Aug 17, 2024	Anomaly DetectionEEG	CodeCode Available	1	5
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning	Sep 12, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization	Apr 8, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1	5
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers	Apr 12, 2021	Anomaly Detection	CodeCode Available	1	5
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection	Feb 3, 2023	Anomaly DetectionDomain Adaptation	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