论文标题
DC-NAS：划分和构造神经建筑搜索
DC-NAS: Divide-and-Conquer Neural Architecture Search
论文作者
论文摘要
大多数应用程序都需要高性能的深神经体系结构成本有限的资源。神经体系结构搜索是一种自动在给定巨大搜索空间中探索最佳深层神经网络的方式。但是，通常使用相同的标准评估所有子网络。也就是说，早期停止了一小部分培训数据集，这是一种不准确且高度复杂的方法。与常规方法相反，在这里，我们提出了一种分裂和诱导方法（DC），以有效，有效地搜索深层神经体系结构。给定一个任意搜索空间，我们首先根据每一层的参数或输出特征的变化提取所有子网络的特征表示，然后根据表示基于表示形式计算两个不同采样网络之间的相似性。然后，进行k均值聚类以将相似的架构汇总到同一群集中，并在每个群集中分别执行子网络评估。后来合并每个集群中的最佳体系结构以获得最佳的神经体系结构。在几个基准上进行的实验结果表明，DC-NAS可以克服不准确的评估问题，在Imagenet数据集上达到了75.1 \％$ $ TOP-1的准确性，该数据集比使用同一搜索空间的最先进方法更高。
Most applications demand high-performance deep neural architectures costing limited resources. Neural architecture searching is a way of automatically exploring optimal deep neural networks in a given huge search space. However, all sub-networks are usually evaluated using the same criterion; that is, early stopping on a small proportion of the training dataset, which is an inaccurate and highly complex approach. In contrast to conventional methods, here we present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures. Given an arbitrary search space, we first extract feature representations of all sub-networks according to changes in parameters or output features of each layer, and then calculate the similarity between two different sampled networks based on the representations. Then, a k-means clustering is conducted to aggregate similar architectures into the same cluster, separately executing sub-network evaluation in each cluster. The best architecture in each cluster is later merged to obtain the optimal neural architecture. Experimental results conducted on several benchmarks illustrate that DC-NAS can overcome the inaccurate evaluation problem, achieving a $75.1\%$ top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.
