Unsupervised Learning for Real-World Super-Resolution
這篇論文是19年的一篇通過非成對圖像的思想解決超分中採用雙三次插值構造數據集訓練的網絡對真實世界的LR圖像泛化能力不強的問題。 在此我用20年最新的非成對圖像超分論文與這篇論文進行比較討論，首先這篇論文與20年的最大區別在於將退化過程與超分過程分開進行訓練，因此在這篇論文中退化的學習過程是無標籤的，退化過程包含兩個可學習的層，一個爲G（學習生成真實世界LR圖像的過程，即通過對抗向LR圖像添加模糊核
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相關文章
- 1. Momentum Contrast for Unsupervised Visual Representation Learning
- 2. Supervised learning and Unsupervised learning
- 3. Introduction - Unsupervised Learning
- 4. Clustering - Unsupervised learning introduction
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- 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(精讀)
- 10. 【MoCo】《Momentum Contrast for Unsupervised Visual Representation Learning》