  Differentiable architecture search (DARTS) provided a fast solution in
finding effective network architectures, but suffered from large memory and
computing overheads in jointly training a super-network and searching for an
optimal architecture. In this paper, we present a novel approach, namely,
Partially-Connected DARTS, by sampling a small part of super-network to reduce
the redundancy in exploring the network space, thereby performing a more
efficient search without comprising the performance. In particular, we perform
operation search in a subset of channels while bypassing the held out part in a
shortcut. This strategy may suffer from an undesired inconsistency on selecting
the edges of super-net caused by sampling different channels. We alleviate it
using edge normalization, which adds a new set of edge-level parameters to
reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can
be trained with a larger batch size and, consequently, enjoys both faster speed
and higher training stability. Experimental results demonstrate the
effectiveness of the proposed method. Specifically, we achieve an error rate of
2.57% on CIFAR10 with merely 0.1 GPU-days for architecture search, and a
state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile
setting) using 3.8 GPU-days for search. Our code has been made available at:
https://github.com/yuhuixu1993/PC-DARTS.
