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Differentiable Architecture Search (DARTS) is a recent neural architecturesearch (NAS) method based on a differentiable relaxation. Due to its success,numerous variants analyzing and improving parts of the DARTS framework haverecently been proposed. By considering the problem as a constrained bileveloptimization, we present and analyze DARTS-PRIME, a variant includingimprovements to architectural weight update scheduling and regularizationtowards discretization. We propose a dynamic schedule based on per-minibatchnetwork information to make architecture updates more informed, as well asproximity regularization to promote well-separated discretization. Our resultsin multiple domains show that DARTS-PRIME improves both performance andreliability, comparable to state-of-the-art in differentiable NAS.

DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS