Learning to map between ferns with differentiable binary embedding networks
Computer Vision and Pattern Recognition
2020-05-29 v1
Abstract
Current deep learning methods are based on the repeated, expensive application of convolutions with parameter-intensive weight matrices. In this work, we present a novel concept that enables the application of differentiable random ferns in end-to-end networks. It can then be used as multiplication-free convolutional layer alternative in deep network architectures. Our experiments on the binary classification task of the TUPAC'16 challenge demonstrate improved results over the state-of-the-art binary XNOR net and only slightly worse performance than its 2x more parameter intensive floating point CNN counterpart.
Cite
@article{arxiv.2005.12563,
title = {Learning to map between ferns with differentiable binary embedding networks},
author = {Max Blendowski and Mattias P. Heinrich},
journal= {arXiv preprint arXiv:2005.12563},
year = {2020}
}