the color of generated pic is not consistent with orginal
llf1234 opened this issue · 7 comments
thanks a lot,recently i train a model with your provided code and data,i find the color of generated eyes is not always consistent with the original input(test on other pic which is not in columbia dataset),when i cancel all instance normalization layer in generator, the color of generated eyes become approximate consitent with the original input? did you pay attention on this phenomenon.
I don't really understand what do you mean by 'cancel the instance normalisation'. And I also don't know how many samples have you tested on, and what kind of metric did you use to measure the color shift. But generally speaking, the color shift problem is sort of a common problem for CycleGAN-based approaches (see Figure. 9 in the original CycleGAN paper). It can be alleviated by adding a L_{identity} loss. And also, I wouldn't expect the model could generalise that well on wild images, as ColumbiaGaze only contains 56 subjects. If a dataset with more subjects is publicly available, I believe the model could get an even better performance.
You could probably find this work https://arxiv.org/pdf/1712.03999.pdf helpful.
Hi @llf1234,
I also face the inconsistent color problem.
Did you remove all the instance_norm in the generator to solve the inconsistent color problem?
Or only remove the instance_norm with the scope "in_conv_%d"
Thanks!
How to solve the inconsistent color problem, you can read this paper with coarse-to-fine learning