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Cross-channel Communication Networks 2019-12-13 14:17:18 Paper: https://papers.nips.cc/paper/8411-cross-channel-communication-networks.pdf Code: https://github.com/jwyang/C3Net.pytorch SENet (Squeeze-and-Excitation Networks): http://openaccess.thecvf…
1. 摘要 在图像超分辨领域,卷积神经网络的深度非常重要,但过深的网络却难以训练.低分辨率的输入以及特征包含丰富的低频信息,但却在通道间被平等对待,因此阻碍了网络的表示能力. 为了解决上述问题,作者提出了一个深度残差通道注意力网络(RCAN).特别地,作者设计了一个残差中的残差(RIR)结构来构造深层网络,每个 RIR 结构由数个残差组(RG)以及长跳跃连接(LSC)组成,每个 RG 则包含一些残差块和短跳跃连接(SSC). RIR 结构允许丰富的低频信息通过多个跳跃连接直接进行传播,使主网络专…
欢迎转载,转载请注明:本文出自Bin的专栏blog.csdn.net/xbinworld. 技术交流QQ群:433250724,欢迎对算法.技术感兴趣的同学加入. 最近接下来几篇博文会回到神经网络结构的讨论上来,前面我在"深度学习方法(五):卷积神经网络CNN经典模型整理Lenet,Alexnet,Googlenet,VGG,Deep Residual Learning"一文中介绍了经典的CNN网络结构模型,这些可以说已经是家喻户晓的网络结构,在那一文结尾,我提到"是时候动一…
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