Learning Invariant Features by Harnessing the Aperture Problem
Abstract
The energy model is a simple, biologically inspired approach to extracting relationships between images in tasks like stereopsis and motion analysis. We discuss how adding an extra pooling layer to the energy model makes it possible to learn encodings of transformations that are mostly invariant with respect to image content, and to learn encodings of images that are mostly invariant with respect to the observed transformations. We show how this makes it possible to learn 3D pose-invariant features of objects by watching videos of the objects. We test our approach on a dataset of videos derived from the NORB dataset.
Cite
Text
Memisevic and Exarchakis. "Learning Invariant Features by Harnessing the Aperture Problem." International Conference on Machine Learning, 2013.Markdown
[Memisevic and Exarchakis. "Learning Invariant Features by Harnessing the Aperture Problem." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/memisevic2013icml-learning/)BibTeX
@inproceedings{memisevic2013icml-learning,
title = {{Learning Invariant Features by Harnessing the Aperture Problem}},
author = {Memisevic, Roland and Exarchakis, Georgios},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {100-108},
volume = {28},
url = {https://mlanthology.org/icml/2013/memisevic2013icml-learning/}
}