High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs個人理解
看了這篇論文 覺得有趣的地方 希望能爲後者提供些不同看法，不足之處希望各位指正 一、 該論文通過改進提高了生成圖像的像素，有趣的一點在於作者能通過low-dimension feature channel 作爲生成器的輸入來實現實例級的目標修改，效果如下圖： 論文中該方法的實現， 1）作者訓練一個生成器網絡E去找圖片中每個實例對應的低維特徵，E是一個encoder-decoder標準結構網絡。 2
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相關文章
- 1. 論文閱讀High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- 2. 《High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs》論文筆記
- 3. Semantic Image Synthesis with Spatially-Adaptive Normalization（CVPR19）
- 4. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 5. Image-to-Image Translation with Conditional Adversarial NetWorks 翻譯
- 6. invertible Conditional GANs for image editing
- 7. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 8. LinkNet with Pretrained Encoder and Dilated Convolution for HighResolution Satellite Image
- 9. Generative Adversarial Text to Image Synthesis
- 10. Image Captioning with Semantic Attention