Optimizing Convolutional Neural Network Architecture via Information Field

CNN architecture design has attracted tremendous attention of improving model accuracy or reducing model complexity. However, existing works either introduce repeated training overhead in the search process or lack an interpretable metric to guide the design. To clear the hurdles, we propose Information Field (IF), 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 IF, 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. Experiments 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.