  Backbone architectures of most binary networks are well-known floating point
architectures such as the ResNet family. Questioning that the architectures
designed for floating point networks would 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 proposed 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.
