  DARTS is a popular algorithm for neural architecture search (NAS). Despite
its great advantage in search efficiency, DARTS often suffers weak stability,
which reflects in the large variation among individual trials as well as the
sensitivity to the hyper-parameters of the search process. This paper owes such
instability to an optimization gap between the super-network and its
sub-networks, namely, improving the validation accuracy of the super-network
does not necessarily lead to a higher expectation on the performance of the
sampled sub-networks. Then, we point out that the gap is due to the inaccurate
estimation of the architectural gradients, based on which we propose an amended
estimation method. Mathematically, our method guarantees a bounded error from
the true gradients while the original estimation does not. Our approach bridges
the gap from two aspects, namely, amending the estimation on the architectural
gradients, and unifying the hyper-parameter settings in the search and
re-training stages. Experiments on CIFAR10 and ImageNet demonstrate that our
approach largely improves search stability and, more importantly, enables
DARTS-based approaches to explore much larger search spaces that have not been
investigated before.
