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Based on the weight-sharing mechanism, one-shot NAS methods train a supernetand then inherit the pre-trained weights to evaluate sub-models, largelyreducing the search cost. However, several works have pointed out that theshared weights suffer from different gradient descent directions duringtraining. And we further find that large gradient variance occurs duringsupernet training, which degrades the supernet ranking consistency. To mitigatethis issue, we propose to explicitly minimize the gradient variance of thesupernet training by jointly optimizing the sampling distributions of PAth andDAta (PA&DA). We theoretically derive the relationship between the gradientvariance and the sampling distributions, and reveal that the optimal samplingprobability is proportional to the normalized gradient norm of path andtraining data. Hence, we use the normalized gradient norm as the importanceindicator for path and training data, and adopt an importance sampling strategyfor the supernet training. Our method only requires negligible computation costfor optimizing the sampling distributions of path and data, but achieves lowergradient variance during supernet training and better generalizationperformance for the supernet, resulting in a more consistent NAS. We conductcomprehensive comparisons with other improved approaches in various searchspaces. Results show that our method surpasses others with more reliableranking performance and higher accuracy of searched architectures, showing theeffectiveness of our method. Code is available athttps://github.com/ShunLu91/PA-DA.

PA&DA: Jointly Sampling PAth and DAta for Consistent NAS