Improved Auto-Encoding using Deterministic Projected Belief Networks
Abstract
In this paper, we exploit the unique properties of a deterministic projected belief network (D-PBN) to take full advantage of trainable compound activation functions (TCAs). A D-PBN is a type of auto-encoder that operates by "backing up" through a feed-forward neural network. TCAs are activation functions with complex monotonic-increasing shapes that change the distribution of the data so that the linear transformation that follows is more effective. Because a D-PBN operates by "backing up", the TCAs are inverted in the reconstruction process, restoring the original distribution of the data, thus taking advantage of a given TCA in both analysis and reconstruction. In this paper, we show that a D-PBN auto-encoder with TCAs can significantly out-perform standard auto-encoders including variational auto-encoders.
Cite
@article{arxiv.2309.07481,
title = {Improved Auto-Encoding using Deterministic Projected Belief Networks},
author = {Paul M Baggenstoss},
journal= {arXiv preprint arXiv:2309.07481},
year = {2023}
}