I did the encoding like the author of this github has mentioned.
VGG = Perceptual model
G = Generator model
G.style() is the fully connected encoder in generator which gives dlatent code.
First question
After optimizing perceptual loss between G and VGG, do I need to save the weights of G.style(), or pass random vector to G.style() save the output from G.style() which is dlatent.
Second Question
If the first approach is right, than I proceed by getting attributes of the face using microsoft cognitive api. I tried on just 860 images to begin with.
I do get the result but it is wrong.
I did the encoding like the author of this github has mentioned. VGG = Perceptual model G = Generator model G.style() is the fully connected encoder in generator which gives dlatent code.
First question After optimizing perceptual loss between G and VGG, do I need to save the weights of G.style(), or pass random vector to G.style() save the output from G.style() which is dlatent.
Second Question If the first approach is right, than I proceed by getting attributes of the face using microsoft cognitive api. I tried on just 860 images to begin with. I do get the result but it is wrong.![image](https://user-images.githubusercontent.com/32990329/55182756-c24e4680-5164-11e9-967a-b47d8bc9df54.png)