{"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/transfer-learning-for-illustration","title":"Transfer Learning for Illustration Classification","arxiv_id":"1806.02682","date":"2018-05-23","proceeding":null,"authors":["Manuel Lagunas","Elena Garces"],"abstract":"The field of image classification has shown an outstanding success thanks to\nthe development of deep learning techniques. Despite the great performance\nobtained, most of the work has focused on natural images ignoring other domains\nlike artistic depictions. In this paper, we use transfer learning techniques to\npropose a new classification network with better performance in illustration\nimages. Starting from the deep convolutional network VGG19, pre-trained with\nnatural images, we propose two novel models which learn object representations\nin the new domain. Our optimized network will learn new low-level features of\nthe images (colours, edges, textures) while keeping the knowledge of the\nobjects and shapes that it already learned from the ImageNet dataset. Thus,\nrequiring much less data for the training. We propose a novel dataset of\nillustration images labelled by content where our optimized architecture\nachieves $\\textbf{86.61\\%}$ of top-1 and $\\textbf{97.21\\%}$ of top-5 precision.\nWe additionally demonstrate that our model is still able to recognize objects\nin photographs.","url_abs":"http://arxiv.org/abs/1806.02682v1","url_pdf":"http://arxiv.org/pdf/1806.02682v1.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":"transfer-learning-for-illustration","repo_url":"https://github.com/MathiasStensrud/capstone-2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}