WESPE: Weakly Supervised Photo Enhancer for Digital Cameras論文模型理解
相對於之前的圖像質量提升方法，本方法無需和低端質量圖像相關的高質量監督數據。訓練時只需要提供需要提高到的圖像質量參考。 x爲輸入低端質量圖像，x通過G之後生成提升後的圖像y~=G（x），再經過一個反向generator生成重構到的低端圖，x和再通過vgg19網絡得到content loss.訓練過程中，訓練G和F使得content loss 足夠小，之所以這裏使用vgg19網絡，是因爲原圖和重構圖
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相關文章
- 1. WESPE的論文與代碼心得（1）
- 2. 閱讀論文《Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs》
- 3. 論文解讀：Object Instance Mining for Weakly Supervised Object Detection（AAAI 2020）
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- 6. Weakly Supervised Deep Detection Networks 論文筆記
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- 8. 【論文筆記】Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detecti
- 9. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation 論文筆記
- 10. 論文筆記（五） C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection