  Network spaces have been known as a critical factor in both handcrafted
network designs or defining search spaces for Neural Architecture Search (NAS).
However, an effective space involves tremendous prior knowledge and/or manual
effort, and additional constraints are required to discover efficiency-aware
architectures. In this paper, we define a new problem, Network Space Search
(NSS), as searching for favorable network spaces instead of a single
architecture. We propose an NSS method to directly search for efficient-aware
network spaces automatically, reducing the manual effort and immense cost in
discovering satisfactory ones. The resultant network spaces, named Elite
Spaces, are discovered from Expanded Search Space with minimal human expertise
imposed. The Pareto-efficient Elite Spaces are aligned with the Pareto front
under various complexity constraints and can be further served as NAS search
spaces, benefiting differentiable NAS approaches (e.g. In CIFAR-100, an
averagely 2.3% lower error rate and 3.7% closer to target constraint than the
baseline with around 90% fewer samples required to find satisfactory networks).
Moreover, our NSS approach is capable of searching for superior spaces in
future unexplored spaces, revealing great potential in searching for network
spaces automatically. Website: https://minhungchen.netlify.app/publication/nss.
