Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. In this paper, we propose G-EA, a novel approach for guided evolutionary NAS. The rationale behind G-EA, is to explore the search space by generating and evaluating several architectures in each generation at initialization stage using a zero-proxy estimator, where only the highest-scoring network is trained and kept for the next generation. This evaluation at initialization stage allows continuous extraction of knowledge from the search space without increasing computation, thus allowing the search to be efficiently guided. Moreover, G-EA forces exploitation of the most performant networks by descendant generation while at the same time forcing exploration by parent mutation and by favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method, showing that G-EA achieves state-of-the-art results in NAS-Bench-201 search space in CIFAR-10, CIFAR-100 and ImageNet16-120, with mean accuracies of 93.98%, 72.12% and 45.94% respectively.
翻译:然而,NAS方法往往很复杂,一旦生成的建筑似乎产生良好结果,往往会与当地小型工程相汇而成。在本文中,我们提议GEA,这是指导进化NAS的一种新颖方法。G-EA背后的理由是,在初始阶段,利用零代代代体测量仪,在每代人中生成和评估若干建筑,在每代人中探索空间,在零代代代代代体测量仪中,只有最高分层网络才经过培训和保留给下一代人。在初始阶段,NAS-Bench-201号搜索空间的这一评估允许在不增加计算的情况下不断从搜索空间提取知识,从而能够有效地指导搜索工作。此外,G-EA部队利用后裔一代人最有才能的网络,同时迫使父母进行突变,并赞成较年轻的建筑来损害老年人。实验结果显示了拟议方法的有效性,表明GEA在CFAR-10、CIFAR-100和图像网络的93-120%和图像网络中分别实现了45.12%和72%的中的最新结果。