{"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/domain-adaptive-transfer-learning-with","title":"Domain Adaptive Transfer Learning with Specialist Models","arxiv_id":"1811.07056","date":"2018-11-16","proceeding":null,"authors":["Jiquan Ngiam","Daiyi Peng","Vijay Vasudevan","Simon Kornblith","Quoc V. Le","Ruoming Pang"],"abstract":"Transfer learning is a widely used method to build high performing computer\nvision models. In this paper, we study the efficacy of transfer learning by\nexamining how the choice of data impacts performance. We find that more\npre-training data does not always help, and transfer performance depends on a\njudicious choice of pre-training data. These findings are important given the\ncontinued increase in dataset sizes. We further propose domain adaptive\ntransfer learning, a simple and effective pre-training method using importance\nweights computed based on the target dataset. Our method to compute importance\nweights follow from ideas in domain adaptation, and we show a novel application\nto transfer learning. Our methods achieve state-of-the-art results on multiple\nfine-grained classification datasets and are well-suited for use in practice.","url_abs":"http://arxiv.org/abs/1811.07056v2","url_pdf":"http://arxiv.org/pdf/1811.07056v2.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":[],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-image-classification-on-stanford","task":"Fine-Grained Image Classification","dataset":"Stanford Cars","model":"DAT","rank_in_archive_order":5,"of":83,"metrics":{"Accuracy":"96.2%"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.07056","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}