SOTAVerified

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

Showing 38763900 of 4856 papers

TitleStatusHype
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types0
Traffic congestion anomaly detection and prediction using deep learning0
Fixing Inventory Inaccuracies At Scale0
Anomaly Detection in Medical Imaging with Deep Perceptual AutoencodersCode1
Locally Masked Convolution for Autoregressive ModelsCode1
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization0
G2D: Generate to Detect Anomaly0
Manifolds for Unsupervised Visual Anomaly DetectionCode1
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and FeaturesCode1
The Clever Hans Effect in Anomaly Detection0
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition SoundsCode1
Use of in-the-wild images for anomaly detection in face anti-spoofing0
Lightweight Collaborative Anomaly Detection for the IoT using BlockchainCode0
Analytical Probability Distributions and EM-Learning for Deep Generative Networks0
Self-Supervised Representation Learning for Visual Anomaly Detection0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction0
Plug-and-Play Anomaly Detection with Expectation Maximization Filtering0
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior0
Anomalous Motion Detection on Highway Using Deep LearningCode0
Learning Latent Space Energy-Based Prior ModelCode1
Robust Variational Autoencoder for Tabular Data with Beta Divergence0
Categorical anomaly detection in heterogeneous data using minimum description length clustering0
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio EstimationCode0
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literatureCode1
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified