In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks. Unlike the conventional NAS problem, our problem requires to search for a unique computational cell called the SPD cell. This SPD cell serves as a basic building block of SPD neural architectures. An efficient solution to our problem is important to minimize the extraneous manual effort in the SPD neural architecture design. To accomplish this goal, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem using the supernet strategy which models the architecture search problem as a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and NAS algorithms. Empirical results show that our algorithm excels in discovering better SPD network design, and providing models that are more than 3 times lighter than searched by state-of-the-art NAS algorithms.

本文提出了一种新的神经架构搜索问题——对对称正定（SPD）流形网络的自动设计，介绍了一个几何丰富且多样化的SPD神经架构搜索空间及其搜索算法，统计评估结果表明该算法在无人机、行动和情感识别任务方面的表现优于现有的网络及传统算法，并且提供的模型比现有算法搜索的模型轻三倍以上。

SPD 流形网络的神经架构搜索