{"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/when-nas-meets-trees-an-efficient-algorithm","title":"When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search","arxiv_id":"2204.04918","date":"2022-04-11","proceeding":null,"authors":["Guocheng Qian","Xuanyang Zhang","Guohao Li","Chen Zhao","Yukang Chen","Xiangyu Zhang","Bernard Ghanem","Jian Sun"],"abstract":"The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces an architecture tree and a binary operation tree, to factorize the search space and substantially reduce the exploration size. TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture. Impressively, TNAS finds the global optimal architecture on CIFAR-10 with test accuracy of 94.37\\% in four GPU hours in NAS-Bench-201. The average test accuracy is 94.35\\%, which outperforms the state-of-the-art. Code is available at: \\url{https://github.com/guochengqian/TNAS}.","url_abs":"https://arxiv.org/abs/2204.04918v1","url_pdf":"https://arxiv.org/pdf/2204.04918v1.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":"when-nas-meets-trees-an-efficient-algorithm","repo_url":"https://github.com/guochengqian/tnas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/neural-architecture-search-on-nas-bench-201-1","task":"Neural Architecture Search","dataset":"NAS-Bench-201, CIFAR-10","model":"TNAS","rank_in_archive_order":9,"of":37,"metrics":{"Accuracy (Test)":"94.35"},"uses_additional_data":false},{"leaderboard":"/sota/neural-architecture-search-on-nas-bench-201-2","task":"Neural Architecture Search","dataset":"NAS-Bench-201, CIFAR-100","model":"TNAS","rank_in_archive_order":12,"of":40,"metrics":{"Accuracy (Test)":"73.02"},"uses_additional_data":false},{"leaderboard":"/sota/neural-architecture-search-on-nas-bench-201","task":"Neural Architecture Search","dataset":"NAS-Bench-201, ImageNet-16-120","model":"TNAS","rank_in_archive_order":17,"of":49,"metrics":{"Accuracy (Test)":"46.31"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}