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 351–360 of 4856 papers

Title	Date	Tasks	Status	Hype
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling	Nov 21, 2023	Anomaly DetectionTime Series	CodeCode Available	1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation	Nov 20, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector	Nov 20, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution	Nov 19, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection	Nov 16, 2023	Anomaly DetectionDepth Anomaly Detection and Segmentation	CodeCode Available	1
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects	Nov 11, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information	Nov 9, 2023	Anomaly DetectionRetrieval	CodeCode Available	1
The voraus-AD Dataset for Anomaly Detection in Robot Applications	Nov 8, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection	Nov 6, 2023	Anomaly DetectionGenerative Adversarial Network	CodeCode Available	1
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection	Nov 5, 2023	Anomaly DetectionQuestion Answering	CodeCode Available	1

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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