论文标题
汇总设计原理的神经结构空间的视觉分析
Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles
论文作者
论文摘要
人工智能的最新进展在很大程度上受益于更好的神经网络体系结构。这些体系结构是经过昂贵的试验过程的产物。为了简化此过程，我们开发了Archexplorer，这是一种视觉分析方法，用于了解神经体系结构空间并汇总设计原理。我们方法背后的关键思想是通过利用体系结构之间的结构距离来解释建筑空间。我们将成对距离计算提出求解，以解决全对最短路径问题。为了提高效率，我们将此问题分解为一组最短的路径问题。时间复杂性从O（KN^2n）降低到O（KNN）。根据它们之间的距离，在层次上聚集体系结构。已经开发了一个基于圆圈的架构可视化，以传达群集和每个集群中架构的本地社区之间的全球关系。提出了两项案例研究和一项分析后，以证明Argsplorer在总结设计原理和选择表现更好的体系结构中的有效性。
Recent advances in artificial intelligence largely benefit from better neural network architectures. These architectures are a product of a costly process of trial-and-error. To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles. The key idea behind our method is to make the architecture space explainable by exploiting structural distances between architectures. We formulate the pairwise distance calculation as solving an all-pairs shortest path problem. To improve efficiency, we decompose this problem into a set of single-source shortest path problems. The time complexity is reduced from O(kn^2N) to O(knN). Architectures are hierarchically clustered according to the distances between them. A circle-packing-based architecture visualization has been developed to convey both the global relationships between clusters and local neighborhoods of the architectures in each cluster. Two case studies and a post-analysis are presented to demonstrate the effectiveness of ArchExplorer in summarizing design principles and selecting better-performing architectures.
