论文标题
一种优化卷积神经网络的有效定量方法
An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks
论文作者
论文摘要
随着深度学习的日益普及，卷积神经网络（CNN）已被广泛应用于各种领域，例如图像分类和对象检测，并在其在传统统计方法上的高精度方面取得了惊人的成功。为了利用CNN模型的潜力，大量的研究和行业努力致力于优化CNN。在这些努力中，CNN体系结构设计引起了极大的关注，因为它具有提高模型准确性或降低模型复杂性的巨大潜力。但是，现有工作要么在搜索过程中引入重复的培训开销，要么缺少可解释的指标来指导设计。为了清除这些障碍，我们提出了一个可解释且易于计算的度量的3D受体领域（3DRF），以估算CNN体系结构的质量并指导设计的搜索过程。为了验证3DRF的有效性，我们构建了一个静态优化器，以改善阶段级别和内核级别的CNN体系结构。我们的优化器不仅提供了一个清晰且可重复的程序，还可以减轻建筑搜索过程中不必要的培训工作。广泛的实验和研究表明，与Mobilenet和Resnet（例如Mobilenet和Resnet）相比，优化器生成的模型可以提高高达5.47％的准确性和65.38％的参数减免。
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.
