  Weight sharing has become a de facto standard in neural architecture search
because it enables the search to be done on commodity hardware. However, recent
works have empirically shown a ranking disorder between the performance of
stand-alone architectures and that of the corresponding shared-weight networks.
This violates the main assumption of weight-sharing NAS algorithms, thus
limiting their effectiveness. We tackle this issue by proposing a
regularization term that aims to maximize the correlation between the
performance rankings of the shared-weight network and that of the standalone
architectures using a small set of landmark architectures. We incorporate our
regularization term into three different NAS algorithms and show that it
consistently improves performance across algorithms, search-spaces, and tasks.
