Unsupervised Learning for Real-World Super-Resolution
这篇论文是19年的一篇通过非成对图像的思想解决超分中采用双三次插值构造数据集训练的网络对真实世界的LR图像泛化能力不强的问题。 在此我用20年最新的非成对图像超分论文与这篇论文进行比较讨论，首先这篇论文与20年的最大区别在于将退化过程与超分过程分开进行训练，因此在这篇论文中退化的学习过程是无标签的，退化过程包含两个可学习的层，一个为G（学习生成真实世界LR图像的过程，即通过对抗向LR图像添加模糊核
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相关文章
- 1. Momentum Contrast for Unsupervised Visual Representation Learning
- 2. Supervised learning and Unsupervised learning
- 3. Introduction - Unsupervised Learning
- 4. Clustering - Unsupervised learning introduction
- 5. Unsupervised Learning-Auto Encoder
- 6. An Unsupervised Autoregressive Model for Speech Representation Learning
- 7. DEFORM-GAN:AN UNSUPERVISED LEARNING MODEL FOR DEFORMABLE REGISTRATION
- 8. 【IROS2018】Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
- 9. Unsupervised Template Learning for Fine-Grained Object Recognition(精读)
- 10. 【MoCo】《Momentum Contrast for Unsupervised Visual Representation Learning》