[论文笔记]Rethinking the Inception Architecture for Computer Vision
2018-03-15 23:45
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摘要：
本文探索了一种方法，通过尽可能有效的稳定分解卷积核和正则化来利用增加的计算量来逐级增加网络的规模。这样单个网络可以在ImageNet数据集上达到21.2%的top-1错误率和5.6%的top-5错误率。与此同时，通过级联多个网络，可以达到17.3%的top-1错误率和3.5%的top-5错误率。
本文探索了一种方法，通过尽可能有效的稳定分解卷积核和正则化来利用增加的计算量来逐级增加网络的规模。这样单个网络可以在ImageNet数据集上达到21.2%的top-1错误率和5.6%的top-5错误率。与此同时，通过级联多个网络，可以达到17.3%的top-1错误率和3.5%的top-5错误率。
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