  Designing feasible and effective architectures under diverse computation
budgets incurred by different applications/devices is essential for deploying
deep models in practice. Existing methods often perform an independent
architecture search for each target budget, which is very inefficient yet
unnecessary. Moreover, the repeated independent search manner would inevitably
ignore the common knowledge among different search processes and hamper the
search performance. To address these issues, we seek to train a general
architecture generator that automatically produces effective architectures for
an arbitrary budget merely via model inference. To this end, we propose a
Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an
arbitrary budget as input and produces the Pareto optimal architecture for the
target budget. We train NAG by learning the Pareto frontier (i.e., the set of
Pareto optimal architectures) over model performance and computational cost
(e.g., latency). Extensive experiments on three platforms (i.e., mobile, CPU,
and GPU) show the superiority of the proposed method over existing NAS methods.
