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 3851–3860 of 4856 papers

Title	Date	Tasks	Status	Hype
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection	Jul 14, 2020	Anomaly DetectionDecoder	—Unverified	0
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure	Jul 12, 2020	Anomaly DetectionAttribute	CodeCode Available	1
Anomaly Detection-Based Unknown Face Presentation Attack Detection	Jul 11, 2020	Anomaly DetectionFace Presentation Attack Detection	CodeCode Available	1
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection	Jul 10, 2020	Anomaly DetectionOpen Set Learning	—Unverified	0
Learning Retrospective Knowledge with Reverse Reinforcement Learning	Jul 9, 2020	Anomaly Detectionreinforcement-learning	—Unverified	0
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network	Jul 9, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends	Jul 9, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network	Jul 8, 2020	Anomaly DetectionAstronomy	CodeCode Available	0
Few-Shot One-Class Classification via Meta-Learning	Jul 8, 2020	Anomaly DetectionClassification	CodeCode Available	1
Deep Learning for Anomaly Detection: A Review	Jul 6, 2020	Anomaly DetectionDeep Learning	—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	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