RARTS: An Efficient First-Order Relaxed Architecture Search Method. (arXiv:2008.03901v2 [cs.LG] UPDATED)

Differentiable architecture search (DARTS) is an effective method for
data-driven neural network design based on solving a bilevel optimization
problem. Despite its success in many architecture search tasks, there are still
some concerns about the accuracy of first-order DARTS and the efficiency of the
second-order DARTS. In this paper, we formulate a single level alternative and
a relaxed architecture search (RARTS) method that utilizes the whole dataset in
architecture learning via both data and network splitting, without involving
mixed second …

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