  Current neural architecture search (NAS) algorithms still require expert
knowledge and effort to design a search space for network construction. In this
paper, we consider automating the search space design to minimize human
interference, which however faces two challenges: the explosive complexity of
the exploration space and the expensive computation cost to evaluate the
quality of different search spaces. To solve them, we propose a novel
differentiable evolutionary framework named AutoSpace, which evolves the search
space to an optimal one with following novel techniques: a differentiable
fitness scoring function to efficiently evaluate the performance of cells and a
reference architecture to speedup the evolution procedure and avoid falling
into sub-optimal solutions. The framework is generic and compatible with
additional computational constraints, making it feasible to learn specialized
search spaces that fit different computational budgets. With the learned search
space, the performance of recent NAS algorithms can be improved significantly
compared with using previously manually designed spaces. Remarkably, the models
generated from the new search space achieve 77.8% top-1 accuracy on ImageNet
under the mobile setting (MAdds < 500M), out-performing previous SOTA
EfficientNet-B0 by 0.7%. All codes will be made public.
