论文标题
蝙蝠：二进制体系结构搜索
BATS: Binary ArchitecTure Search
论文作者
论文摘要
本文提出了二进制体系结构搜索（蝙蝠），该框架通过神经体系结构搜索（NAS）大大降低了二进制神经网络及其真实价值对应物之间的准确性差距。我们表明，直接将NAS应用于二元域可提供非常差的结果。为了减轻这一点，我们首次描述了成功将NAS应用于二进制域的三种关键要素。具体而言，我们（1）介绍和设计一个新型的面向二进制的搜索空间，（2）提出了一种用于控制和稳定所得搜索拓扑的新机制，（3）提出并验证了一系列二进制网络的新搜索策略，以导致更快的收敛性和较低的搜索时间。实验结果证明了所提出的方法的有效性以及直接在二进制空间中搜索的必要性。此外，（4）我们在CIFAR10，CIFAR100和Imagenet数据集上设置了一个新的针对二进制神经网络的最新最新技术。代码将提供https://github.com/1adrianb/binary-nas
This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS). We show that directly applying NAS to the binary domain provides very poor results. To alleviate this, we describe, to our knowledge, for the first time, the 3 key ingredients for successfully applying NAS to the binary domain. Specifically, we (1) introduce and design a novel binary-oriented search space, (2) propose a new mechanism for controlling and stabilising the resulting searched topologies, (3) propose and validate a series of new search strategies for binary networks that lead to faster convergence and lower search times. Experimental results demonstrate the effectiveness of the proposed approach and the necessity of searching in the binary space directly. Moreover, (4) we set a new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and ImageNet datasets. Code will be made available https://github.com/1adrianb/binary-nas
