AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
下載鏈接：https://arxiv.org/pdf/1707.01400.pdf 一 、什麼是對抗？ 對抗樣本和對抗網絡 所謂對抗，樣本是指將實際樣本略加擾動而構造出的合成樣本，對該樣本，分類器非常容易將其類別判錯，這意味着光滑性假設(相似的樣本應該以很高的概率被判爲同一類別)某種程度上被推翻了。 有一篇論文應該是最早提出對抗樣本概念的。該論文指出，包括卷積神經網
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相關文章
- 1. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 3. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 4. Conditional Generative Adversarial Networks（CGAN）筆記
- 5. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 6. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 7. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 8. Generative Adversarial Text to Image Synthesis
- 9. Image-to-Image Translation with Conditional Adversarial Networks
- 10. Image-to-Image Translation with Conditional Adversarial Networks筆記