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 3301–3310 of 4856 papers

Title	Date	Tasks	Status
Time Series Data Augmentation for Deep Learning: A Survey	Feb 27, 2020	Anomaly DetectionData Augmentation	—Unverified
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters	Dec 30, 2022	Anomaly DetectionTime Series	—Unverified
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies	Apr 2, 2021	Anomaly DetectionDynamic Time Warping	—Unverified
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms	May 14, 2020	Anomaly DetectionDeep Learning	—Unverified
Time topological analysis of EEG using signature theory	Apr 6, 2024	Anomaly DetectionEEG	—Unverified
TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera	Aug 27, 2021	Anomaly DetectionHuman Detection	—Unverified
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT	Mar 7, 2023	Anomaly DetectionTime Series	—Unverified
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection	Jul 1, 2024	Anomaly DetectionContrastive Learning	—Unverified
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation	Sep 16, 2017	Anomaly DetectionNavigate	—Unverified
Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping	Mar 17, 2024	Anomaly DetectionDecision Making	—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