AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
下载链接：https://arxiv.org/pdf/1707.01400.pdf 一 、什么是对抗？ 对抗样本和对抗网络 所谓对抗，样本是指将实际样本略加扰动而构造出的合成样本，对该样本，分类器非常容易将其类别判错，这意味着光滑性假设(相似的样本应该以很高的概率被判为同一类别)某种程度上被推翻了。 有一篇论文应该是最早提出对抗样本概念的。该论文指出，包括卷积神经网
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相关文章
- 1. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. 【翻译】Image-to-Image Translation with Conditional Adversarial Networks
- 3. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 4. Conditional Generative Adversarial Networks（CGAN）笔记
- 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
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