Local Binary Pattern Networks

Memory and computation efficient deep learning architectures are crucial to continued proliferation of machine learning capabilities to new platforms and systems. Binarization of operations in convolutional neural networks has shown promising results in reducing model size and computing efficiency. In this paper, we tackle the problem using 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. LBPNet uses local binary comparisons and random projection in place of conventional convolution (or approximation of convolution) operations. These operations can be implemented efficiently on different platforms including direct hardware implementation. We applied LBPNet and its variants on standard benchmarks. The results are promising across benchmarks while providing an important means to improve memory and speed efficiency that is particularly suited for small footprint devices and hardware accelerators.