Competitive Anti-Hebbian Learning of Invariants
Abstract
Although the detection of invariant structure in a given set of input patterns is vital to many recognition tasks, connectionist learning rules tend to focus on directions of high variance (principal components). The prediction paradigm is often used to reconcile this dichotomy; here we suggest a more direct approach to invariant learning based on an anti-Hebbian learning rule. An unsupervised tWO-layer network implementing this method in a competitive setting learns to extract coherent depth information from random-dot stereograms.
Cite
Text
Schraudolph and Sejnowski. "Competitive Anti-Hebbian Learning of Invariants." Neural Information Processing Systems, 1991.Markdown
[Schraudolph and Sejnowski. "Competitive Anti-Hebbian Learning of Invariants." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/schraudolph1991neurips-competitive/)BibTeX
@inproceedings{schraudolph1991neurips-competitive,
title = {{Competitive Anti-Hebbian Learning of Invariants}},
author = {Schraudolph, Nicol N. and Sejnowski, Terrence J.},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {1017-1024},
url = {https://mlanthology.org/neurips/1991/schraudolph1991neurips-competitive/}
}