论文阅读笔记：Perceptual Losses for Real-Time Style Transfer and Super-Resolution
这篇文章是ECCV2016的一篇文章，主要是提出了一种全新的Loss用于图像风格转换和图像超分辨。 整体框架如上图所示，分为两个网络，图像转换网络和损失网络，其中图像转换网络是深度残差网络，将图片转换为^ y = fW (x) 。我们训练图像转换网络来最小化损失网络的输出值加权和。损失网络部分部分是16层的VGG网络，它的特定层拿出来作为损失函数。 我们将这个损失函数与这两个函数作对比实验。在图像
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相关文章
- 1. 论文解读之Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 2. Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 3. Perceptual Losses for Real-Time Style Transfer and Super-Resolution阅读（一）
- 4. Perceptual Losses for Real-Time Style Transfer and Super-Resolution阅读（二）
- 5. Perceptual Losses for Real-Time Style Transfer and Super-Resolution and Super-Resolution 论文笔记
- 6. Perceptual Losses对SR论文阅读笔记
- 7. 论文《Perceptual Losses for Real-Time Style Transfer and Super-Resolution》
- 8. [Style Transfer]——Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 9. [論文筆記] Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 10. 项目实训（二十四）Perceptual Losses for Real-Time Style Transfer and Super-Resolution阅读