  Neural architecture search (NAS) methods have been proposed to release human
experts from tedious architecture engineering. However, most current methods
are constrained in small-scale search due to the issue of computational
resources. Meanwhile, directly applying architectures searched on small
datasets to large datasets often bears no performance guarantee. This
limitation impedes the wide use of NAS on large-scale tasks. To overcome this
obstacle, we propose an elastic architecture transfer mechanism for
accelerating large-scale neural architecture search (EAT-NAS). In our
implementations, architectures are first searched on a small dataset, e.g.,
CIFAR-10. The best one is chosen as the basic architecture. The search process
on the large dataset, e.g., ImageNet, is initialized with the basic
architecture as the seed. The large-scale search process is accelerated with
the help of the basic architecture. What we propose is not only a NAS method
but a mechanism for architecture-level transfer.
  In our experiments, we obtain two final models EATNet-A and EATNet-B that
achieve competitive accuracies, 74.7% and 74.2% on ImageNet, respectively,
which also surpass the models searched from scratch on ImageNet under the same
settings. For the computational cost, EAT-NAS takes only less than 5 days on 8
TITAN X GPUs, which is significantly less than the computational consumption of
the state-of-the-art large-scale NAS methods.
