  Neural Architecture Search (NAS) aims to automatically find effective
architectures within a predefined search space. However, the search space is
often extremely large. As a result, directly searching in such a large search
space is non-trivial and also very time-consuming. To address the above issues,
in each search step, we seek to limit the search space to a small but effective
subspace to boost both the search performance and search efficiency. To this
end, we propose a novel Neural Architecture Search method via Dominative
Subspace Mining (DSM-NAS) that finds promising architectures in automatically
mined subspaces. Specifically, we first perform a global search, i.e .,
dominative subspace mining, to find a good subspace from a set of candidates.
Then, we perform a local search within the mined subspace to find effective
architectures. More critically, we further boost search performance by taking
well-designed/ searched architectures to initialize candidate subspaces.
Experimental results demonstrate that DSM-NAS not only reduces the search cost
but also discovers better architectures than state-of-the-art methods in
various benchmark search spaces.
