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 4181–4190 of 4856 papers

Title	Date	Tasks	Status
RATE-DISTORTION OPTIMIZATION GUIDED AUTOENCODER FOR GENERATIVE APPROACH	Sep 25, 2019	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Adversarially learned anomaly detection for time series data	Sep 25, 2019	Anomaly DetectionTime Series	—Unverified
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders	Sep 25, 2019	Anomaly DetectionOutlier Detection	—Unverified
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning	Sep 25, 2019	Anomaly DetectionDensity Estimation	—Unverified
On unsupervised-supervised risk and one-class neural networks	Sep 25, 2019	Anomaly DetectionClustering	—Unverified
Deep End-to-end Unsupervised Anomaly Detection	Sep 25, 2019	Anomaly DetectionClustering	—Unverified
Input complexity and out-of-distribution detection with likelihood-based generative models	Sep 25, 2019	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available
When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations	Sep 25, 2019	Anomaly Detection	—Unverified
Combining Machine Learning Models using combo Library	Sep 21, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Genetic Neural Architecture Search for automatic assessment of human sperm images	Sep 20, 2019	Anomaly DetectionNeural Architecture Search	—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