Generative Adversarial Network-based Image Super-Resolution using Perceptual Content Losses
這個周開始寫隨筆記錄下看的論文（本文僅爲本人防止自己忘記寫的禁止轉載） 該論文主要思路是超分時圖像可能出現失真或者過平滑致使圖像細節模糊的狀況，而近期發現感知（特徵）和失真可以存在一中折中的情況既不至於失真但細節完整的情況，因此就這個提出了一個模型（大部分都是別人的模型）。 首先時RB塊由兩個卷積層和一個激活函數relu組成殘差網絡其實後面還有個relu沒畫出來因爲殘差操作是發生在線性映射之後非線
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相關文章
- 1. 《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》閱讀筆記
- 2. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
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- 4. 論文閱讀之《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》
- 5. Binary Generative Adversarial Networks for Image Retrieval
- 6. Generative Adversarial Text to Image Synthesis
- 7. CycleGAN:Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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- 9. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
- 10. Generative Adversarial Networks for Hyperspectral Image Classification