Learning to Traverse Image Manifolds
Abstract
We present a new algorithm, Locally Smooth Manifold Learning (LSML), that learns a warping function from a point on an manifold to its neighbors. Important characteristics of LSML include the ability to recover the structure of the manifold in sparsely populated regions and beyond the support of the provided data. Appli- cations of our proposed technique include embedding with a natural out-of-sample extension and tasks such as tangent distance estimation, frame rate up-conversion, video compression and motion transfer.
Cite
Text
Dollár et al. "Learning to Traverse Image Manifolds." Neural Information Processing Systems, 2006.Markdown
[Dollár et al. "Learning to Traverse Image Manifolds." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/dollar2006neurips-learning/)BibTeX
@inproceedings{dollar2006neurips-learning,
title = {{Learning to Traverse Image Manifolds}},
author = {Dollár, Piotr and Rabaud, Vincent and Belongie, Serge J.},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {361-368},
url = {https://mlanthology.org/neurips/2006/dollar2006neurips-learning/}
}