ShuffleNet构建在MobileNetV2的倒置瓶颈模块之上,他以为深度可分离卷积中的点式卷积会牺牲准确性,以换取更少的计算量。为了补偿这一点,ShuffleNet添加了一个额外的通道改组操作,以确保逐点卷积不会一直运用于相反的“点”。在ShuffleNetV2中,此通道重排机制也进一步扩展到ResNet身份映射分支,因此身份功能的一部分也将用于重排。
2018:Bag of Tricks
随着EfficientNet的发布,ImageNet分类基准似乎即将结束。运用现有的深度学习方法,除非发生另一种形式转变,否则我们永远不会有一天可以在ImageNet上达到99.999%的准确性。因此,研讨人员正在积极研讨一些新颖的范畴,例如用于大规模视觉辨认的自我监督或半监督学习。同时,运用现有方法,对于工程师和企业家来说,找到这种不完美技术的实践运用曾经成为一个成绩。 后台私信回复“20200821”获取论文大礼包
Reference
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https://towardsdatascience.com/10-papers-you-should-read-to-understand-image-classification-in-the-deep-learning-era-4b9d792f45a7end