A Style-Based Generator Architecture for Generative Adversarial Networks 翻譯3&4
3.基於樣式的生成器的屬性 我們的生成器架構使得通過對樣式進行特定比例的修改來控制圖像合成成爲可能。我們可以將映射網絡和仿射變換視爲從學習分佈中爲每個樣式提取樣本的方法，而將合成網絡視爲基於樣式集合生成新圖像的方法。每個樣式的效果都在網絡中本地化，即修改樣式的特定子集可能會影響只有圖像的某些方面。 爲了瞭解這種局部化的原因，讓我們考慮AdaIN操作（公式1）如何首先將每個信道規格化爲零平均值和單位
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- 2. [Style Transfer]——A Style-Based Generator Architecture for Generative Adversarial Networks
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- 8. ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks論文翻譯——中英文對照
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相關文章
- 1. A Style-Based Generator Architecture for Generative Adversarial Networks 翻譯1&2
- 2. [Style Transfer]——A Style-Based Generator Architecture for Generative Adversarial Networks
- 3. A Style-Based Generator Architecture for Generative Adversarial Networks---閱讀筆記
- 4. style-GAN：A Style-Based Generator Architecture for Generative Adversarial Networks. (一)
- 5. Binary Generative Adversarial Networks for Image Retrieval
- 6. Generative Adversarial Networks for Hyperspectral Image Classification
- 7. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 8. ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks論文翻譯——中英文對照
- 9. ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing 原文翻譯
- 10. Graphical Generative Adversarial Networks