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.

翻译：神经结构搜索(NAS) 指的是自动设计建筑。 我们建议对此问题采取由小时玻璃启发的方法(HourNAS), 其动机是建筑的影响往往从关键几个区块开始。 在从输入到深神经网络输出的保障路径中, 关键区块的颈部很窄, 从输入到深神经网络输出, 限制信息流动和影响网络的准确性。 其它区块占据网络的主要体积, 并确定整个网络的复杂性, 与一个小时玻璃的灯泡相对应。 为了在保持高度准确性的同时实现极快的NAS, 我们提议确定关键区块, 并把它们作为建筑搜索的优先事项。 这些非虚拟区块的搜索空间进一步缩小, 只覆盖在计算资源限制下负担得起的候选人。 图像网络的实验结果显示, 我们的HourNAS能够搜索一个达到77.0%的顶端-1精确度的建筑, 从而超越了最先进的方法。