  Numerous valuable efforts have been devoted to achieving arbitrary style
transfer since the seminal work of Gatys et al. However, existing
state-of-the-art approaches often generate insufficiently stylized results
under challenging cases. We believe a fundamental reason is that these
approaches try to generate the stylized result in a single shot and hence fail
to fully satisfy the constraints on semantic structures in the content images
and style patterns in the style images. Inspired by the works on
error-correction, instead, we propose a self-correcting model to predict what
is wrong with the current stylization and refine it accordingly in an iterative
manner. For each refinement, we transit the error features across both the
spatial and scale domain and invert the processed features into a residual
image, with a network we call Error Transition Network (ETNet). The proposed
model improves over the state-of-the-art methods with better semantic
structures and more adaptive style pattern details. Various qualitative and
quantitative experiments show that the key concept of both progressive strategy
and error-correction leads to better results. Code and models are available at
https://github.com/zhijieW94/ETNet.
