  Searching for a more compact network width recently serves as an effective
way of channel pruning for the deployment of convolutional neural networks
(CNNs) under hardware constraints. To fulfill the searching, a one-shot
supernet is usually leveraged to efficiently evaluate the performance
\wrt~different network widths. However, current methods mainly follow a
\textit{unilaterally augmented} (UA) principle for the evaluation of each
width, which induces the training unfairness of channels in supernet. In this
paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet)
to address this issue. In BCNet, each channel is fairly trained and responsible
for the same amount of network widths, thus each network width can be evaluated
more accurately. Besides, we leverage a stochastic complementary strategy for
training the BCNet, and propose a prior initial population sampling method to
boost the performance of the evolutionary search. Extensive experiments on
benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve
state-of-the-art or competing performance over other baseline methods.
Moreover, our method turns out to further boost the performance of NAS models
by refining their network widths. For example, with the same FLOPs budget, our
obtained EfficientNet-B0 achieves 77.36\% Top-1 accuracy on ImageNet dataset,
surpassing the performance of original setting by 0.48\%.
