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 141–150 of 4856 papers

Title	Date	Tasks	Status	Hype
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective	Sep 16, 2022	Anomaly DetectionAutoML	CodeCode Available	2
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation	Jul 28, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	2
Registration based Few-Shot Anomaly Detection	Jul 15, 2022	Anomaly Detection	CodeCode Available	2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise	Jun 30, 2022	Anomaly DetectionDenoising	CodeCode Available	2
A Unified Model for Multi-class Anomaly Detection	Jun 8, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	2
Rethinking Graph Neural Networks for Anomaly Detection	May 31, 2022	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	2
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities	May 2, 2022	Anomaly DetectionDefect Detection	CodeCode Available	2
Log-based Anomaly Detection with Deep Learning: How Far Are We?	Feb 9, 2022	Anomaly DetectionDeep Learning	CodeCode Available	2
Anomaly Detection via Reverse Distillation from One-Class Embedding	Jan 26, 2022	Anomaly Classification	CodeCode Available	2
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	Jan 18, 2022	Anomaly DetectionMeta-Learning	CodeCode Available	2

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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