DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks閱讀筆記
深度注意生成對抗網絡的實例級圖像翻譯 一、圖像翻譯 通過從一個圖像域到另一個圖像域找到一些底層對應關係（例如，類似的語義）來學習映射函數，這就是圖像翻譯問題。翻譯的原則是保留源域的預期身份（例如，文本到圖像中的語義，面對動畫中的人類ID），同時生成與目標域的分佈匹配的樣本。 二、目前技術挑戰 多年的研究已經在有足夠的配對的監督環境中產生了強大的翻譯系統。然而，獲得配對的訓練數據是困難且昂貴的。因此
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相關文章
- 1. DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks閱讀筆記
- 2. DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks
- 3. Image-to-Image Translation with Conditional Adversarial Networks筆記
- 4. BEGAN: Boundary Equilibrium Generative Adversarial Networks閱讀筆記
- 5. Generative Adversarial Networks: An Overview文獻閱讀筆記
- 6. Self-Attention Generative Adversarial Networks
- 7. 【閱讀筆記】Generative Adversarial Nets
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- 9. SAGAN(Self-Attention Generative Adversarial Networks)論文筆記
- 10. SAGAN——Self-Attention Generative Adversarial Networks