  One-shot NAS method has attracted much interest from the research community
due to its remarkable training efficiency and capacity to discover high
performance models. However, the search spaces of previous one-shot based works
usually relied on hand-craft design and were short for flexibility on the
network topology. In this work, we try to enhance the one-shot NAS by exploring
high-performing network architectures in our large-scale Topology Augmented
Search Space (i.e., over 3.4*10^10 different topological structures).
Specifically, the difficulties for architecture searching in such a complex
space has been eliminated by the proposed stabilized share-parameter proxy,
which employs Stochastic Gradient Langevin Dynamics to enable fast shared
parameter sampling, so as to achieve stabilized measurement of architecture
performance even in search space with complex topological structures. The
proposed method, namely Stablized Topological Neural Architecture Search
(ST-NAS), achieves state-of-the-art performance under Multiply-Adds (MAdds)
constraint on ImageNet. Our lite model ST-NAS-A achieves 76.4% top-1 accuracy
with only 326M MAdds. Our moderate model ST-NAS-B achieves 77.9% top-1 accuracy
just required 503M MAdds. Both of our models offer superior performances in
comparison to other concurrent works on one-shot NAS.
