每日论文image-to-Image Translation with Conditional Adversarial Networks
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- 这篇文章设计一个有条件的GANs,文中的loss function 用了L1与ground truth距离而不是L2,减少了模糊。
- 文中还用了skips，避免遇到网络结构上的瓶颈丢失了输入图像的重要信息。
- 在判别器上，为了使得图像能高频化，文中将图片变成局部块，这样判别器只是惩罚局部的块，至于连接就用到了另一篇论文的思想，随机马尔代夫方法连接。
- 为了更好优化网络，一步D，一步G，用最小SGD和Adam solver方法。
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