  Most applications demand high-performance deep neural architectures costing
limited resources. Neural architecture searching is a way of automatically
exploring optimal deep neural networks in a given huge search space. However,
all sub-networks are usually evaluated using the same criterion; that is, early
stopping on a small proportion of the training dataset, which is an inaccurate
and highly complex approach. In contrast to conventional methods, here we
present a divide-and-conquer (DC) approach to effectively and efficiently
search deep neural architectures. Given an arbitrary search space, we first
extract feature representations of all sub-networks according to changes in
parameters or output features of each layer, and then calculate the similarity
between two different sampled networks based on the representations. Then, a
k-means clustering is conducted to aggregate similar architectures into the
same cluster, separately executing sub-network evaluation in each cluster. The
best architecture in each cluster is later merged to obtain the optimal neural
architecture. Experimental results conducted on several benchmarks illustrate
that DC-NAS can overcome the inaccurate evaluation problem, achieving a
$75.1\%$ top-1 accuracy on the ImageNet dataset, which is higher than that of
state-of-the-art methods using the same search space.
