论文标题
小时：通过沙漏镜头搜索的神经建筑极快
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
论文作者
论文摘要
神经体系结构搜索（NAS）是指自动设计体系结构。我们提出了一种由沙漏风格的方法（小时）来解决这个问题，这是由于建筑的影响通常从重要的几个障碍物而产生的。像沙漏的狭窄颈部一样，从输入到深神经网络输出的保证路径中的重要块限制了信息流并影响网络的准确性。其他块占据了网络的主要体积并确定整体网络复杂性，与沙漏的灯泡相对应。为了在保留高精度的同时获得非常快的NA，我们建议确定重要的块并使它们成为架构搜索的优先级。这些非重要区块的搜索空间进一步缩小，仅涵盖计算资源限制下负担得起的候选人。 ImageNet上的实验结果表明，仅使用3个小时（0.1天）使用一个GPU，我们的HourNAS可以搜索达到77.0％Top-1精度的体系结构，这表现出色，从而超出了最先进的方法。
Neural Architecture Search (NAS) refers to automatically design the architecture. We propose an hourglass-inspired approach (HourNAS) for this problem that is motivated by the fact that the effects of the architecture often proceed from the vital few blocks. Acting like the narrow neck of an hourglass, vital blocks in the guaranteed path from the input to the output of a deep neural network restrict the information flow and influence the network accuracy. The other blocks occupy the major volume of the network and determine the overall network complexity, corresponding to the bulbs of an hourglass. To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search. The search space of those non-vital blocks is further shrunk to only cover the candidates that are affordable under the computational resource constraints. Experimental results on the ImageNet show that only using 3 hours (0.1 days) with one GPU, our HourNAS can search an architecture that achieves a 77.0% Top-1 accuracy, which outperforms the state-of-the-art methods.
