  The use of automatic methods, often referred to as Neural Architecture Search
(NAS), in designing neural network architectures has recently drawn
considerable attention. In this work, we present an efficient NAS approach,
named HM- NAS, that generalizes existing weight sharing based NAS approaches.
Existing weight sharing based NAS approaches still adopt hand-designed
heuristics to generate architecture candidates. As a consequence, the space of
architecture candidates is constrained in a subset of all possible
architectures, making the architecture search results sub-optimal. HM-NAS
addresses this limitation via two innovations. First, HM-NAS incorporates a
multi-level architecture encoding scheme to enable searching for more flexible
network architectures. Second, it discards the hand-designed heuristics and
incorporates a hierarchical masking scheme that automatically learns and
determines the optimal architecture. Compared to state-of-the-art weight
sharing based approaches, HM-NAS is able to achieve better architecture search
performance and competitive model evaluation accuracy. Without the constraint
imposed by the hand-designed heuristics, our searched networks contain more
flexible and meaningful architectures that existing weight sharing based NAS
approaches are not able to discover.
