计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 312-318.doi: 10.11896/jsjkx.211200036
孙长迪, 潘志松, 张艳艳
SUN Chang-di, PAN Zhi-song, ZHANG Yan-yan
摘要: 近年来,随着无人驾驶、无人机智能化、移动互联网的发展,低功耗、低算力的移动和嵌入式平台对轻量化的神经网络需求日益迫切。文中基于可变形卷积和深度可分离卷积思想的启发,提出了一种低开销可变形卷积,其兼具了可变形卷积的高效特征提取能力和深度可分离卷积的低运算量的优点。此外,在应用低开销可变形卷积的基础上,结合模型结构压缩的方法,设计了4种MobileNet网络再轻量化的方法。在Caltech256,CIFAR100和CIFAR10数据集上进行了实验,结果表明,低开销可变形卷积在运算量增加不明显的情况下,可以有效提高轻量级网络的分类准确度。并且,结合所提出的4种MobileNet再轻量化方法,可以将MobileNet网络的准确度提高0.4%~1%,与此同时网络运算量可以下降5%~15%,即显著提高了轻量化网络的各项性能,更加符合低功耗、低算力的现实需求,对于移动和嵌入式平台领域的智能化推进有着很重要的现实意义。
中图分类号:
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