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 3391–3400 of 4856 papers

Title	Date	Tasks	Status
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection	Feb 20, 2023	Anomaly DetectionDecoder	—Unverified
UaiNets: From Unsupervised to Active Deep Anomaly Detection	May 1, 2019	Anomaly DetectionDeep Learning	—Unverified
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance	Nov 5, 2020	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges	Sep 25, 2023	Anomaly DetectionDense Video Captioning	—Unverified
Ultrafast single-channel machine vision based on neuro-inspired photonic computing	Feb 15, 2023	Anomaly Detection	—Unverified
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving	Jun 10, 2024	Anomaly DetectionAutonomous Driving	—Unverified
UMGAD: Unsupervised Multiplex Graph Anomaly Detection	Nov 19, 2024	Anomaly DetectionAttribute	—Unverified
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator	Oct 22, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Uncertainty-Aware Deep Classifiers using Generative Models	Jun 7, 2020	Anomaly Detection	—Unverified
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior	May 22, 2024	Anomaly Detection	—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	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