  Layer assignment is seldom picked out as an independent research topic in
neural architecture search. In this paper, for the first time, we
systematically investigate the impact of different layer assignments to the
network performance by building an architecture dataset of layer assignment on
CIFAR-100. Through analyzing this dataset, we discover a neural inheritance
relation among the networks with different layer assignments, that is, the
optimal layer assignments for deeper networks always inherit from those for
shallow networks. Inspired by this neural inheritance relation, we propose an
efficient one-shot layer assignment search approach via inherited sampling.
Specifically, the optimal layer assignment searched in the shallow network can
be provided as a strong sampling priori to train and search the deeper ones in
supernet, which extremely reduces the network search space. Comprehensive
experiments carried out on CIFAR-100 illustrate the efficiency of our proposed
method. Our search results are strongly consistent with the optimal ones
directly selected from the architecture dataset. To further confirm the
generalization of our proposed method, we also conduct experiments on
Tiny-ImageNet and ImageNet. Our searched results are remarkably superior to the
handcrafted ones under the unchanged computational budgets. The neural
inheritance relation discovered in this paper can provide insights to the
universal neural architecture search.
