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 821–830 of 4856 papers

Title	Date	Tasks	Status	Hype
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks	Sep 30, 2024	Anomaly DetectionTransfer Learning	—Unverified	0
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset	Sep 30, 2024	Anomaly Detection	CodeCode Available	1
Constraining Anomaly Detection with Anomaly-Free Regions	Sep 30, 2024	Anomaly Detection	—Unverified	0
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection	Sep 30, 2024	Anomaly DetectionLanguage Modeling	—Unverified	0
Novel machine learning applications at the LHC	Sep 30, 2024	Anomaly Detection	—Unverified	0
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach	Sep 30, 2024	Anomaly Detection	CodeCode Available	0
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI	Sep 29, 2024	Anomaly DetectionDenoising	CodeCode Available	1
Sparse Modelling for Feature Learning in High Dimensional Data	Sep 28, 2024	Anomaly DetectionDefect Detection	—Unverified	0
Kinematic Detection of Anomalies in Human Trajectory Data	Sep 27, 2024	Anomaly Detection	CodeCode Available	0
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models	Sep 27, 2024	Anomaly DetectionKnowledge Distillation	—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	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