Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
網絡結構 本文的GAN網絡結構爲: 生成網絡的輸入爲需要風格轉換的圖像即input,以及風格特徵.採用VGG16/19的fc1層,提取風格圖像的特徵,風格特徵爲4096維的向量. 生成網絡結構和目標函數 文章試驗發現,如果u-net可以使用底層的網絡學習到特徵,那麼高層的網絡就不會去學習,如圖4所示,u-net網絡的輸入輸出都爲同一張圖像,也就是實現複製圖像的功能.由於輸入輸出是相同的,損失函數會
相關文章
- 1. [Style Transfer]——GANs for Medical Image Analysis
- 2. Style transfer GAN
- 3. [Style Transfer]——Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 4. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)
- 5. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 6. Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 7. [Style Transfer]——Adversarial Stain Transfer for Histopathology Image Analysis
- 8. [Style Transfer]——Neural Style Transfer: A Review
- 9. SR-Enhanced Deep Residual Networks for Single Image Super-Resolution
- 10. Arbitrary Style Transfer with Deep Feature Reshuffle
- 更多相關文章...
- • ASP Transfer 方法 - ASP 教程
- • ASP.NET Style 控件 - ASP.NET 教程
- • RxJava操作符（七）Conditional and Boolean
- • 算法總結-股票買賣
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. [Style Transfer]——GANs for Medical Image Analysis
- 2. Style transfer GAN
- 3. [Style Transfer]——Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 4. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)
- 5. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 6. Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 7. [Style Transfer]——Adversarial Stain Transfer for Histopathology Image Analysis
- 8. [Style Transfer]——Neural Style Transfer: A Review
- 9. SR-Enhanced Deep Residual Networks for Single Image Super-Resolution
- 10. Arbitrary Style Transfer with Deep Feature Reshuffle