Maximum likelihood features for generative image models

DOI10.1214/17-AOAS1025zbMath1380.62238OpenAlexW2763907698MaRDI QIDQ1684214

Eran Borenstein, Wei Zhang, Stuart Geman, Lo-Bin Chang

Publication date: 8 December 2017

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/17-aoas1025

zbMATH Keywords

featurescomputer visionsufficiencygenerative modelsimage modelsappearance modelsconditional modeling

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Bayesian inference (62F15) Image analysis in multivariate analysis (62H35)

Related Items (2)

Intraday pairs trading strategies on high frequency data: the case of oil companies ⋮ On the rate of convergence of image classifiers based on convolutional neural networks

This page was built for publication: Maximum likelihood features for generative image models