  One-shot Neural Architecture Search (NAS) has been widely used to discover
architectures due to its efficiency. However, previous studies reveal that
one-shot performance estimations of architectures might not be well correlated
with their performances in stand-alone training because of the excessive
sharing of operation parameters (i.e., large sharing extent) between
architectures. Thus, recent methods construct even more over-parameterized
supernets to reduce the sharing extent. But these improved methods introduce a
large number of extra parameters and thus cause an undesirable trade-off
between the training costs and the ranking quality. To alleviate the above
issues, we propose to apply Curriculum Learning On Sharing Extent (CLOSE) to
train the supernet both efficiently and effectively. Specifically, we train the
supernet with a large sharing extent (an easier curriculum) at the beginning
and gradually decrease the sharing extent of the supernet (a harder
curriculum). To support this training strategy, we design a novel supernet
(CLOSENet) that decouples the parameters from operations to realize a flexible
sharing scheme and adjustable sharing extent. Extensive experiments demonstrate
that CLOSE can obtain a better ranking quality across different computational
budget constraints than other one-shot supernets, and is able to discover
superior architectures when combined with various search strategies. Code is
available at https://github.com/walkerning/aw_nas.
