  With the increasing popularity of deep learning, Convolutional Neural
Networks (CNNs) have been widely applied in various domains, such as image
classification and object detection, and achieve stunning success in terms of
their high accuracy over the traditional statistical methods. To exploit the
potential of CNN models, a huge amount of research and industry efforts have
been devoted to optimizing CNNs. Among these endeavors, CNN architecture design
has attracted tremendous attention because of its great potential of improving
model accuracy or reducing model complexity. However, existing work either
introduces repeated training overhead in the search process or lacks an
interpretable metric to guide the design. To clear these hurdles, we propose
3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to
estimate the quality of a CNN architecture and guide the search process of
designs. To validate the effectiveness of 3DRF, we build a static optimizer to
improve the CNN architectures at both the stage level and the kernel level. Our
optimizer not only provides a clear and reproducible procedure but also
mitigates unnecessary training efforts in the architecture search process.
Extensive experiments and studies show that the models generated by our
optimizer can achieve up to 5.47% accuracy improvement and up to 65.38%
parameters deduction, compared with state-of-the-art CNN structures like
MobileNet and ResNet.
