Face Aging with Contextual Generative Adversarial Nets
網絡結構如圖2所示,首先按照68個人臉特徵點對輸入圖像進行對齊,之後,採用Deeplab v2算法將輸入圖像分割爲 人臉區域,和非人臉區域,並將非人臉區域標記爲灰色. 生成網絡-Transformer network 將處理後的圖像,與年齡lable y y 輸入到條件變換網絡(G,Transformer),得到合成圖像 G(x,y) G ( x , y ) (fake image). 生成網絡結
相關文章
- 1. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 3. Generative Adversarial Nets[BEGAN]
- 4. Generative Adversarial Nets[pix2pix]
- 5. Generative Adversarial Nets[CycleGAN]
- 6. Conditional Generative Adversarial Nets
- 7. Generative Adversarial Nets（譯）
- 8. paper：Conditional Generative Adversarial Nets
- 9. Generative Adversarial Nets
- 10. cGAN:Conditional Generative Adversarial Nets
- 更多相關文章...
- • XSLT
- • C# 基本語法 - C#教程
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
- • 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. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 3. Generative Adversarial Nets[BEGAN]
- 4. Generative Adversarial Nets[pix2pix]
- 5. Generative Adversarial Nets[CycleGAN]
- 6. Conditional Generative Adversarial Nets
- 7. Generative Adversarial Nets（譯）
- 8. paper：Conditional Generative Adversarial Nets
- 9. Generative Adversarial Nets
- 10. cGAN:Conditional Generative Adversarial Nets