Network binarization is a promising hardware-aware direction for creating
efficient deep models. Despite its memory and computational advantages,
reducing the accuracy gap between binary models and their real-valued
counterparts remains an unsolved challenging research problem. To this end, we
make the following 3 contributions: (a) To increase model capacity, we propose
Expert Binary Convolution, which, for the first time, tailors conditional
computing to binary networks by learning to select one data-specific expert
binary filter at a time conditioned on input features. (b) To increase
representation capacity, we propose to address the inherent information
bottleneck in binary networks by introducing an efficient width expansion
mechanism which keeps the binary operations within the same budget. (c) To
improve network design, we propose a principled binary network growth mechanism
that unveils a set of network topologies of favorable properties. Overall, our
method improves upon prior work, with no increase in computational cost, by
$\sim6 \%$, reaching a groundbreaking $\sim 71\%$ on ImageNet classification.
Code will be made available
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本研究针对网络二值化技术领域的准确度损失问题，提出了三种新方法：1）使用专家二值卷积提高模型容量，2）引入宽度扩展机制以提高表示容量，3）提出了一种可行的网络增长机制以获得良好的网络拓扑结构，通过实验在 ImageNet 分类任务上取得了超过以往方法 6% 的精度提升。