  The ability to rank candidate architectures is the key to the performance of
neural architecture search~(NAS). One-shot NAS is proposed to reduce the
expense but shows inferior performance against conventional NAS and is not
adequately stable. We investigate into this and find that the ranking
correlation between architectures under one-shot training and the ones under
stand-alone full training is poor, which misleads the algorithm to discover
better architectures. Further, we show that the training of architectures of
different sizes under the current one-shot method is imbalanced, which causes
the evaluated performances of the architectures to be less predictable of their
ground-truth performances and affects the ranking correlation heavily.
Consequently, we propose Balanced NAO where we introduce balanced training of
the supernet during the search procedure to encourage more updates for large
architectures than small architectures by sampling architectures in proportion
to their model sizes. Comprehensive experiments verify that our proposed method
is effective and robust which leads to a more stable search. The final
discovered architecture shows significant improvements against baselines with a
test error rate of 2.60\% on CIFAR-10 and top-1 accuracy of 74.4% on ImageNet
under the mobile setting. Code and model checkpoints will be publicly
available. The code is available at github.com/renqianluo/NAO_pytorch.
