Learning to map between ferns with differentiable binary embedding networks

Max Blendowski, Mattias P. Heinrich

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.

Paper Type: methodological development

Track: short paper

Keywords: End-To-End Trainable Ferns, Network Efficiency, Binary Embedding

TL;DR: We demonstrate how to implement differentiable random ferns as energy & parameter efficient alternative to convolutional layers.

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