Neural Architecture Search without Training

Joe Mellor · Jack Turner · Amos Storkey · Elliot Crowley

Keywords: [ Architectures ]

[ Abstract ]
[ Paper ]
[ Visit Poster at Spot A4 in Virtual World ]
Tue 20 Jul 9 a.m. PDT — 11 a.m. PDT
Oral presentation: AutoML and Deep Architecture
Tue 20 Jul 6 a.m. PDT — 7 a.m. PDT


The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be slow and expensive; they need to train vast numbers of candidate networks to inform the search process. This could be alleviated if we could partially predict a network's trained accuracy from its initial state. In this work, we examine the overlap of activations between datapoints in untrained networks and motivate how this can give a measure which is usefully indicative of a network’s trained performance. We incorporate this measure into a simple algorithm that allows us to search for powerful networks without any training in a matter of seconds on a single GPU, and verify its effectiveness on NAS-Bench-101, NAS-Bench-201, NATS-Bench, and Network Design Spaces. Our approach can be readily combined with more expensive search methods; we examine a simple adaptation of regularised evolutionary search. Code for reproducing our experiments is available at

Chat is not available.