Dual Pipeline Style Transfer with Input Distribution Differentiation
ShiQi Jiang JunJie Kang YuJian Li
The color and texture dual pipeline architecture (CTDP) suppresses texture
representation and artifacts through masked total variation loss (Mtv), and
further experiments have shown that smooth input can almost completely
eliminate texture representation. We have demonstrated through experiments that
smooth input is not the key reason for removing texture representations, but
rather the distribution differentiation of the training dataset. Based on this,
we propose an input distribution differentiation training strategy (IDD), which
forces the generation of textures to be completely dependent on the noise
distribution, while the smooth distribution will not produce textures at all.
Overall, our proposed distribution differentiation training strategy allows for
two pre-defined input distributions to be responsible for two generation tasks,
with noise distribution responsible for texture generation and smooth
distribution responsible for color smooth transfer. Finally, we choose a smooth
distribution as the input for the forward inference stage to completely
eliminate texture representations and artifacts in color transfer tasks.