  Binary Neural Networks (BNNs), known to be one among the effectively compact
network architectures, have achieved great outcomes in the visual tasks.
Designing efficient binary architectures is not trivial due to the binary
nature of the network. In this paper, we propose a use of evolutionary search
to facilitate the construction and training scheme when binarizing MobileNet, a
compact network with separable depth-wise convolution. Inspired by one-shot
architecture search frameworks, we manipulate the idea of group convolution to
design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an
approximately optimal trade-off between computational cost and model accuracy.
Our objective is to come up with a tiny yet efficient binary neural
architecture by exploring the best candidates of the group convolution while
optimizing the model performance in terms of complexity and latency. The
approach is threefold. First, we train strong baseline binary networks with a
wide range of random group combinations at each convolutional layer. This
set-up gives the binary neural networks a capability of preserving essential
information through layers. Second, to find a good set of hyperparameters for
group convolutions we make use of the evolutionary search which leverages the
exploration of efficient 1-bit models. Lastly, these binary models are trained
from scratch in a usual manner to achieve the final binary model. Various
experiments on ImageNet are conducted to show that following our construction
guideline, the final model achieves 60.09% Top-1 accuracy and outperforms the
state-of-the-art CI-BCNN with the same computational cost.
