{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/glstylenet-higher-quality-style-transfer","title":"GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features","arxiv_id":"1811.07260","date":"2018-11-18","proceeding":null,"authors":["Zhizhong Wang","Lei Zhao","Wei Xing","Dongming Lu"],"abstract":"Recent studies using deep neural networks have shown remarkable success in\nstyle transfer especially for artistic and photo-realistic images. However, the\napproaches using global feature correlations fail to capture small, intricate\ntextures and maintain correct texture scales of the artworks, and the\napproaches based on local patches are defective on global effect. In this\npaper, we present a novel feature pyramid fusion neural network, dubbed\nGLStyleNet, which sufficiently takes into consideration multi-scale and\nmulti-level pyramid features by best aggregating layers across a VGG network,\nand performs style transfer hierarchically with multiple losses of different\nscales. Our proposed method retains high-frequency pixel information and low\nfrequency construct information of images from two aspects: loss function\nconstraint and feature fusion. Our approach is not only flexible to adjust the\ntrade-off between content and style, but also controllable between global and\nlocal. Compared to state-of-the-art methods, our method can transfer not just\nlarge-scale, obvious style cues but also subtle, exquisite ones, and\ndramatically improves the quality of style transfer. We demonstrate the\neffectiveness of our approach on portrait style transfer, artistic style\ntransfer, photo-realistic style transfer and Chinese ancient painting style\ntransfer tasks. Experimental results indicate that our unified approach\nimproves image style transfer quality over previous state-of-the-art methods,\nwhile also accelerating the whole process in a certain extent. Our code is\navailable at https://github.com/EndyWon/GLStyleNet.","url_abs":"http://arxiv.org/abs/1811.07260v1","url_pdf":"http://arxiv.org/pdf/1811.07260v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"glstylenet-higher-quality-style-transfer","repo_url":"https://github.com/EndyWon/GLStyleNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}