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 3371–3380 of 4856 papers

Title	Date	Tasks	Status	Hype
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection	May 22, 2021	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1
Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss	May 21, 2021	Anomaly Detection	—Unverified	0
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks	May 21, 2021	Anomaly Detectionimage-classification	CodeCode Available	0
Multi-Perspective Anomaly Detection	May 20, 2021	Anomaly DetectionDenoising	—Unverified	0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection	May 20, 2021	Anomaly DetectionClustering	CodeCode Available	0
Graph Sanitation with Application to Node Classification	May 19, 2021	Anomaly DetectionBilevel Optimization	—Unverified	0
Masked Contrastive Learning for Anomaly Detection	May 18, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
StackVAE-G: An efficient and interpretable model for time series anomaly detection	May 18, 2021	Anomaly DetectionGraph Learning	CodeCode Available	0
Vision Transformers are Robust Learners	May 17, 2021	Anomaly DetectionImage Classification	CodeCode Available	1
DOC3-Deep One Class Classification using Contradictions	May 17, 2021	Anomaly DetectionClassification	—Unverified	0

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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