Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning. (arXiv:2205.09542v1 [cs.CV])

In this work, we tackle the challenging problem of arbitrary image style
transfer using a novel style feature representation learning method. A suitable
style representation, as a key component in image stylization tasks, is
essential to achieve satisfactory results. Existing deep neural network based
approaches achieve reasonable results with the guidance from second-order
statistics such as Gram matrix of content features. However, they do not
leverage sufficient style information, which results in artifacts such as local
distortions and style inconsistency. …

arxiv cv image learning style transfer transfer