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 2981–2990 of 4856 papers

Title	Date	Tasks	Status	Hype
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms	Jan 26, 2022	Anomaly DetectionFeature Engineering	—Unverified	0
Anomaly Detection via Reverse Distillation from One-Class Embedding	Jan 26, 2022	Anomaly Classification	CodeCode Available	2
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection	Jan 25, 2022	Active LearningAnomaly Detection	—Unverified	0
Community-based anomaly detection using spectral graph filtering	Jan 24, 2022	Anomaly DetectionOutlier Detection	—Unverified	0
COVID-19 Detection Using CT Image Based On YOLOv5 Network	Jan 24, 2022	Anomaly Detection	—Unverified	0
Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices	Jan 24, 2022	Anomaly Detection	—Unverified	0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series	Jan 23, 2022	Anomaly DetectionTime Series	—Unverified	0
Compressed Smooth Sparse Decomposition	Jan 19, 2022	Anomaly Detection	—Unverified	0
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	Jan 18, 2022	Anomaly DetectionMeta-Learning	CodeCode Available	2
Online Time Series Anomaly Detection with State Space Gaussian Processes	Jan 18, 2022	Anomaly DetectionGaussian Processes	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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