解决的问题
前人的做法：
Success of image translation methods mostly imposes the requirement of working on aligned or similar domains for texture or appearance transform.
The building blocks of these networks, such as convolution/deconvolution layers and activation functions, are spatially corresponding.
问题：
As shown in Fig. 1(a-d), artifacts or ghosting could appear when nonsmile and smile faces are not geometrically aligned in image space.
大白话解释一段我的理解：
非成对图像翻译，比如非笑脸图(小明，男)要翻译成笑脸图（小明，男）。
输入网络的图，非笑脸图(小明，男)和笑脸图（大红唇的小红，女），经过一系列的convolution/deconvolution layers and activation functions，得到小明的五官信息和大红唇小红的笑信息（maybe 嘴角上扬图），重组以后得到，大红唇的笑脸小明。
结果图中的大红唇可以看作artifacts or ghosting。
期望的效果：
它的具体做法：
SPM
RM
12个平行采样的残差块，对生成结果进行精细化。
Mask
3 deconvolutional layers to up-sample f into a 1-channel mask m, the same size as the input.
Sigmoid layer is used as the final activation layer to range the output mask in [0; 1].
a regularization term Lm to enforce sparsity of masks in L1-norm:
实验效果展示
实验详情
数据库：
- CelebA (200K 名人图像，40种属性，图像大小218*178)
- CelebA-HQ （图像大小1024*1024）
- RaFD （67个人，8种表情，3种眼神方向，5种拍摄角度）
选择的属性“Smiling’, ‘Arched eyebrow’, ‘Big Nose’, ‘Pointy nose’”，只训练了正脸。
Pytorch
TITAN Xp
训练策略(two-stage training)
ü Adam，learning rate 1e-4
ü LR framework, 128 *128 images, batch size 16, iterations.
ü Higher resolutions 256*256 or 512*512, batch size 8 , another iterations.
