Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

Alessandro Di Martino, Erik Bodin, Carl Henrik Ek

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Original languageUndefined/Unknown
Title of host publicationComputer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV
EditorsC. V. Jawahar, Hongdong Li, Greg Mori, Konrad Schindler
PublisherSpringer
Pages3-20
Number of pages18
Volume11364
DOIs
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Cite this

Martino, A. D., Bodin, E., & Ek, C. H. (2018). Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. In C. V. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV (Vol. 11364, pp. 3-20). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-20870-71