Learning to map between ferns with differentiable binary embedding networks

• URL: http://arxiv.org/abs/2005.12563v1
• Date: Tue, 26 May 2020 08:13:23 GMT
• Title: Learning to map between ferns with differentiable binary embedding networks
• Authors: Max Blendowski and Mattias P. Heinrich
• Abstract summary: 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.
• Score: 4.827284036182784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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