2018/03/19 by Jeng-Hau Lin, Lin, Jeng-Hau, Yunfan Yang +6
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #cs.CV
paper · pdf · doi:10.48550/arxiv.1803.07125
14 pages, 10 figures, 6 tables
arxiv created 2018/03/22 · arxiv updated 2018/03/23
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.