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 3321–3330 of 4856 papers

Title	Date	Tasks	Status
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos	Dec 8, 2021	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing	Dec 8, 2021	Anomaly DetectionDecision Making	—Unverified
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder	Dec 6, 2021	Anomaly DetectionEvent Detection	—Unverified
Online false discovery rate control for anomaly detection in time series	Dec 6, 2021	Anomaly DetectionTime Series	—Unverified
Autoencoders for Semivisible Jet Detection	Dec 6, 2021	Anomaly Detection	—Unverified
Constrained Adaptive Projection with Pretrained Features for Anomaly Detection	Dec 5, 2021	Anomaly Detection	CodeCode Available
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects	Dec 2, 2021	Anomaly Detection	CodeCode Available
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation	Dec 2, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Auditing Keyword Queries Over Text Documents	Dec 1, 2021	Anomaly Detection	—Unverified
CLAWS: Contrastive Learning with hard Attention and Weak Supervision	Dec 1, 2021	Anomaly DetectionContrastive Learning	—Unverified

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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