{"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/revisiting-training-free-nas-metrics-an","title":"Revisiting Training-free NAS Metrics: An Efficient Training-based Method","arxiv_id":"2211.08666","date":"2022-11-16","proceeding":null,"authors":["Taojiannan Yang","Linjie Yang","Xiaojie Jin","Chen Chen"],"abstract":"Recent neural architecture search (NAS) works proposed training-free metrics to rank networks which largely reduced the search cost in NAS. In this paper, we revisit these training-free metrics and find that: (1) the number of parameters (\\#Param), which is the most straightforward training-free metric, is overlooked in previous works but is surprisingly effective, (2) recent training-free metrics largely rely on the \\#Param information to rank networks. Our experiments show that the performance of recent training-free metrics drops dramatically when the \\#Param information is not available. Motivated by these observations, we argue that metrics less correlated with the \\#Param are desired to provide additional information for NAS. We propose a light-weight training-based metric which has a weak correlation with the \\#Param while achieving better performance than training-free metrics at a lower search cost. Specifically, on DARTS search space, our method completes searching directly on ImageNet in only 2.6 GPU hours and achieves a top-1/top-5 error rate of 24.1\\%/7.1\\%, which is competitive among state-of-the-art NAS methods. Codes are available at \\url{https://github.com/taoyang1122/Revisit_TrainingFree_NAS}","url_abs":"https://arxiv.org/abs/2211.08666v1","url_pdf":"https://arxiv.org/pdf/2211.08666v1.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":"revisiting-training-free-nas-metrics-an","repo_url":"https://github.com/taoyang1122/revisit_trainingfree_nas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[{"method_slug":"darts","method_name":"DARTS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.08666","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}