论文标题
SPD歧管网络的神经架构搜索
Neural Architecture Search of SPD Manifold Networks
论文作者
论文摘要
在本文中，我们提出了一个新的神经体系结构搜索（NAS）对称正定（SPD）歧管网络的问题，旨在自动化SPD神经体系结构的设计。为了解决这个问题，我们首先引入了一个几何和多样化的SPD神经架构搜索空间，以实现有效的SPD细胞设计。此外，我们通过单个超级网的单次训练过程对新的NAS问题进行了建模。基于超级网络建模，我们在宽松的连续搜索空间中利用了一种可区分的NAS算法，用于SPD神经体系结构搜索。与最先进的SPD网络和传统NAS算法相比，我们对无人机，动作和情绪识别任务的方法的统计评估大多提供了更好的结果。经验结果表明，我们的算法在发现更好的性能SPD网络设计方面表现出色，并提供了比最先进的NAS算法搜索的三倍以上的模型。
In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, 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 with 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 traditional NAS algorithms. Empirical results show that our algorithm excels in discovering better performing SPD network design and provides models that are more than three times lighter than searched by the state-of-the-art NAS algorithms.
