Learning Architectures for Binary Networks

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). Specifically, based on the cell based search method, we define a new set of layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer to learn well-performing binary networks. In addition, we propose to diversify early search to learn better performing binary architectures. We show that our searched binary networks outperform state-of-the-art binary networks on CIFAR10 and ImageNet datasets.