  Neural architecture search (NAS) can have a significant impact in computer
vision by automatically designing optimal neural network architectures for
various tasks. A variant, binarized neural architecture search (BNAS), with a
search space of binarized convolutions, can produce extremely compressed
models. Unfortunately, this area remains largely unexplored. BNAS is more
challenging than NAS due to the learning inefficiency caused by optimization
requirements and the huge architecture space. To address these issues, we
introduce channel sampling and operation space reduction into a differentiable
NAS to significantly reduce the cost of searching. This is accomplished through
a performance-based strategy used to abandon less potential operations. Two
optimization methods for binarized neural networks are used to validate the
effectiveness of our BNAS. Extensive experiments demonstrate that the proposed
BNAS achieves a performance comparable to NAS on both CIFAR and ImageNet
databases. An accuracy of $96.53\%$ vs. $97.22\%$ is achieved on the CIFAR-10
dataset, but with a significantly compressed model, and a $40\%$ faster search
than the state-of-the-art PC-DARTS.
