Recent years have witnessed a surging interest in Neural Architecture Search (NAS). Various algorithms have been proposed to improve the search efficiency and effectiveness of NAS, i.e., to reduce the search cost and improve the generalization performance of the selected architectures, respectively. However, the search efficiency of these algorithms is severely limited by the need for model training during the search process. To overcome this limitation, we propose a novel NAS algorithm called NAS at Initialization (NASI) that exploits the capability of a Neural Tangent Kernel in being able to characterize the converged performance of candidate architectures at initialization, hence allowing model training to be completely avoided to boost the search efficiency. Besides the improved search efficiency, NASI also achieves competitive search effectiveness on various datasets like CIFAR-10/100 and ImageNet. Further, NASI is shown to be label- and data-agnostic under mild conditions, which guarantees the transferability of architectures selected by our NASI over different datasets.
翻译:近年来,人们对神经结构搜索(NAS)的兴趣日益浓厚,提出了各种算法,以提高NAS的搜索效率和效能,即分别降低搜索成本和提高选定建筑的通用性能,然而,这些算法的搜索效率由于在搜索过程中需要示范培训而受到严重限制。为了克服这一限制,我们提议了一种名为NAS的新型NAS 初始化(NASI)的NAS 算法,它利用Neural Tangnel的能力来描述候选建筑在初始化时的趋同性能,从而使模型培训完全得以避免,以提高搜索效率。除了提高搜索效率外,NASI还在各种数据集,例如CIFAR-10100和图像网络上实现了竞争性搜索效率。此外,还证明NASI在温和的条件下是标签和数据识别的,保证了我们NASI所选择的建筑在不同的数据集上的可转移性。