  Neural Architecture Search (NAS) aims to facilitate the design of deep
networks for new tasks. Existing techniques rely on two stages: searching over
the architecture space and validating the best architecture. NAS algorithms are
currently compared solely based on their results on the downstream task. While
intuitive, this fails to explicitly evaluate the effectiveness of their search
strategies. In this paper, we propose to evaluate the NAS search phase. To this
end, we compare the quality of the solutions obtained by NAS search policies
with that of random architecture selection. We find that: (i) On average, the
state-of-the-art NAS algorithms perform similarly to the random policy; (ii)
the widely-used weight sharing strategy degrades the ranking of the NAS
candidates to the point of not reflecting their true performance, thus reducing
the effectiveness of the search process. We believe that our evaluation
framework will be key to designing NAS strategies that consistently discover
architectures superior to random ones.
