{"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/improved-texture-networks-maximizing-quality","title":"Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis","arxiv_id":"1701.02096","date":"2017-01-09","proceeding":"CVPR 2017 7","authors":["Dmitry Ulyanov","Andrea Vedaldi","Victor Lempitsky"],"abstract":"The recent work of Gatys et al., who characterized the style of an image by\nthe statistics of convolutional neural network filters, ignited a renewed\ninterest in the texture generation and image stylization problems. While their\nimage generation technique uses a slow optimization process, recently several\nauthors have proposed to learn generator neural networks that can produce\nsimilar outputs in one quick forward pass. While generator networks are\npromising, they are still inferior in visual quality and diversity compared to\ngeneration-by-optimization. In this work, we advance them in two significant\nways. First, we introduce an instance normalization module to replace batch\nnormalization with significant improvements to the quality of image\nstylization. Second, we improve diversity by introducing a new learning\nformulation that encourages generators to sample unbiasedly from the Julesz\ntexture ensemble, which is the equivalence class of all images characterized by\ncertain filter responses. Together, these two improvements take feed forward\ntexture synthesis and image stylization much closer to the quality of\ngeneration-via-optimization, while retaining the speed advantage.","url_abs":"http://arxiv.org/abs/1701.02096v2","url_pdf":"http://arxiv.org/pdf/1701.02096v2.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":"improved-texture-networks-maximizing-quality","repo_url":"https://github.com/DmitryUlyanov/texture_nets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-stylization","task_name":"Image Stylization"},{"task_slug":"texture-synthesis","task_name":"Texture Synthesis"}],"methods":[{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.02096","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}