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- GitHub - yulunzhang RCAN: PyTorch code for our ECCV 2018 paper Image . . .
PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks" - GitHub - yulunzhang RCAN: PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"
- [1807. 02758] Image Super-Resolution Using Very Deep Residual Channel . . .
To solve these problems, we propose the very deep residual channel attention networks (RCAN) Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections
- RCAN论文笔记：Image Super-Resolution Using Very Deep Residual Channel . . .
如图2所示，作者提出的RCAN主要由四个部分组成： 浅层特征提取、残差嵌套（RIR）深度特征提取、上采样模块和重建部分。
- 残差通道注意力网络 RCAN - 知乎
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）。
- 超分辨率第六章-RCAN | 沙漠客的学习驿站
RCAN发表于2018年，引入了注意力机制：Channel Attention (CA) 论文地址： Image Super-Resolution Using Very Deep Residual Channel Attention Networks
- GitHub - Lornatang RCAN-PyTorch: PyTorch implements `Image Super . . .
To solve these problems, we propose the very deep residual channel attention networks (RCAN) Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections
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- 【论文笔记_4】【ECCV 2018 超分辨】RCAN - 知乎
为了解决这些问题，我们提出了 深度 残差通道注意网络 （RCAN）。 在RCAN中，我们提出了一种由几个 具有长跳跃连接的残差组 构成的 residual in residual （RIR）结构来构建深度网络。
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