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arXiv · 1803.07125

Local Binary Pattern Networks

Jeng-Hau Lin·Yunfan Yang·Rajesh Gupta·Zhuowen Tu
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Abstract

Memory and computation efficient deep learning architec- tures are crucial to continued proliferation of machine learning capabili- ties to new platforms and systems. Binarization of operations in convo- lutional neural networks has shown promising results in reducing model size and computing efficiency. In this paper, we tackle the problem us- ing a strategy different from the existing literature by proposing local binary pattern networks or LBPNet, that is able to learn and perform binary operations in an end-to-end fashion. LBPNet1 uses local binary comparisons and random projection in place of conventional convolu- tion (or approximation of convolution) operations. These operations can be implemented efficiently on different platforms including direct hard- ware implementation. We applied LBPNet and its variants on standard benchmarks. The results are promising across benchmarks while provid- ing an important means to improve memory and speed efficiency that is particularly suited for small footprint devices and hardware accelerators.

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Jeng-Hau Lin, Yunfan Yang, Rajesh Gupta, Zhuowen Tu. 2018-03-19. Local Binary Pattern Networks. https://arxiv.org/abs/1803.07125

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