论文标题
学习二进制网络的架构
Learning Architectures for Binary Networks
论文作者
论文摘要
大多数二进制网络的骨干架构是众所周知的浮点架构，例如Resnet家族。质疑为浮点网络设计的体系结构对于二进制网络而言并不是最好的，我们建议通过定义用于二进制架构的新搜索空间和新颖的搜索目标来搜索二进制网络（BNA）的体系结构。具体而言，基于基于单元格的搜索方法，我们定义了二进制层类型的新搜索空间，设计新的单元格模板，并重新发现了使用零层的实用程序，而不是将其用作占位符。新颖的搜索目标将尽早搜索多样化，以学习更好的性能二进制体系结构。我们表明，尽管二进制网络固有的量化误差，但我们提出的方法搜索架构具有稳定的训练曲线。定量分析表明，我们的搜索体系结构的表现优于最先进的二进制网络中使用的体系结构，并且表现优于或与最先进的二进制网络相同，这些网络采用了除了体系结构更改以外的各种技术。
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.
