Abstract
Image style transfer aims to apply artists painting styles to various images. Many approaches seek different purposes, but a general tendency is to increase efficiency and enable arbitrary style inputs. The state-of-art method is the adaptive convolutional network which expands the process of feature mixture into a layer-wise adjustment that superior previous work by presenting results that is more aware of detailed structures. However, the encoding process that guides the entire stylized revision is unaware of multi-scaled information. We designed an improved version of the style feature encoding procedure in our work. With the introduction of dilated convolution with a different rate, the output stylized image is better at spatial texture migration and color distribution determination. We also come up with a hybrid objective that better measures the spatial dissimilarity between content and style features.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
