  Backbone architectures of most binary networks are well-known floating point
(FP) architectures such as the ResNet family. Questioning that the
architectures designed for FP networks might not be the best for binary
networks, we propose to search architectures for binary networks (BNAS) by
defining a new search space for binary architectures and a novel search
objective. Specifically, based on the cell based search method, we define the
new search space of binary layer types, design a new cell template, and
rediscover the utility of and propose to use the Zeroise layer instead of using
it as a placeholder. The novel search objective diversifies early search to
learn better performing binary architectures. We show that our method searches
architectures with stable training curves despite the quantization error
inherent in binary networks. Quantitative analyses demonstrate that our
searched architectures outperform the architectures used in state-of-the-art
binary networks and outperform or perform on par with state-of-the-art binary
networks that employ various techniques other than architectural changes. In
addition, we further propose improvements to the training scheme of our
searched architectures. With the new training scheme for our searched
architectures, we achieve the state-of-the-art performance by binary networks
by outperforming all previous methods by non-trivial margins.
