{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:02:40Z","timestamp":1770739360243,"version":"3.49.0"},"reference-count":85,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Shenzhen High-Level Talent Team Program"},{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"publisher","award":["KQTD 20240729102051063"],"award-info":[{"award-number":["KQTD 20240729102051063"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["62025603"],"award-info":[{"award-number":["62025603"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62332002"],"award-info":[{"award-number":["62332002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61825101"],"award-info":[{"award-number":["61825101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2037"],"award-info":[{"award-number":["U21B2037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22B2051"],"award-info":[{"award-number":["U22B2051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20383"],"award-info":[{"award-number":["U23A20383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176222"],"award-info":[{"award-number":["62176222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176223"],"award-info":[{"award-number":["62176223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176226"],"award-info":[{"award-number":["62176226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072386"],"award-info":[{"award-number":["62072386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072387"],"award-info":[{"award-number":["62072387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072389"],"award-info":[{"award-number":["62072389"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002305"],"award-info":[{"award-number":["62002305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272401"],"award-info":[{"award-number":["62272401"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576299"],"award-info":[{"award-number":["62576299"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2021J06003"],"award-info":[{"award-number":["2021J06003"]}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2022J06001"],"award-info":[{"award-number":["2022J06001"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tpami.2025.3629288","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:39:41Z","timestamp":1762367981000},"page":"2398-2412","source":"Crossref","is-referenced-by-count":0,"title":["Good Performance Estimation Strategies are All You Need in Neural Architecture Search"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6855-5403","authenticated-orcid":false,"given":"Xiawu","family":"Zheng","sequence":"first","affiliation":[{"name":"Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binghan","family":"Chen","sequence":"additional","affiliation":[{"name":"Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingkai","family":"Wang","sequence":"additional","affiliation":[{"name":"Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-2638","authenticated-orcid":false,"given":"Fei","family":"Chao","sequence":"additional","affiliation":[{"name":"Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5035-3168","authenticated-orcid":false,"given":"Chenglin","family":"Wu","sequence":"additional","affiliation":[{"name":"DeepWisdom Inc, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0575-6523","authenticated-orcid":false,"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9163-2932","authenticated-orcid":false,"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[{"name":"Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2978-5935","authenticated-orcid":false,"given":"Yonghong","family":"Tian","sequence":"additional","affiliation":[{"name":"School of AI for Science at Shenzhen Graduate School and the School of Computer Science, Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Zero-cost proxies for lightweight NAS","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Abdelfattah","year":"2021"},{"key":"ref2","first-page":"171","article-title":"Adaptive stochastic natural gradient method for one-shot neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Akimoto","year":"2019"},{"key":"ref3","article-title":"Differential evolution for neural architecture search","volume-title":"Proc. Int Conf. Learn. Representations Workshop Neural Archit. Search","author":"Awad","year":"2020"},{"key":"ref4","article-title":"DesigEning neural network architectures using reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Baker","year":"2017"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.29172\/7c2a6982-6d72-4cd8-bba6-2fccb06a7011"},{"key":"ref6","article-title":"Smash: One-shot model architecture search through hypernetworks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Brock","year":"2018"},{"key":"ref7","article-title":"Once for all: Train one network and specialize it for efficient deployment","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai","year":"2020"},{"key":"ref8","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai","year":"2019"},{"key":"ref9","first-page":"482","article-title":"Probabilistic neural architecture search","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Casale","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref11","article-title":"Neural architecture search on imageNet in four GPU hours: A theoretically inspired perspective","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00138"},{"key":"ref13","first-page":"6638","article-title":"DetNAS: Backbone search for object detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2019"},{"key":"ref14","article-title":"DARTS-: Robustly stepping out of performance collapse without indicators","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chu","year":"2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01202"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_28"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00300"},{"key":"ref19","article-title":"NAS-bench-201: Extending the scope of reproducible neural architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dong","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00378"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00186"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"ref23","first-page":"1437","article-title":"BOHB: Robust and efficient hyperparameter optimization at scale","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Falkner","year":"2018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref28","first-page":"28037","article-title":"NAS-bench-suite-zero: Accelerating research on zero cost proxies","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Krishnakumar","year":"2022"},{"key":"ref29","article-title":"SNIP: Single-shot network pruning based on connection sensitivity","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lee","year":"2019"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00169"},{"key":"ref31","article-title":"Geometry-aware gradient algorithms for neural architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2021"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00040"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"ref35","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2019"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86383-8_44"},{"key":"ref37","first-page":"10547","article-title":"Semi-supervised neural architecture search","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Luo","year":"2020"},{"key":"ref38","first-page":"7827","article-title":"Neural architecture optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo","year":"2018"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref40","first-page":"7588","article-title":"Neural architecture search without training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mellor","year":"2021"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00212"},{"key":"ref42","first-page":"12265","article-title":"Evaluating efficient performance estimators of neural architectures","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Ning","year":"2021"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01061"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02008"},{"key":"ref46","first-page":"4095","article-title":"Efficient neural architecture search via parameter sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pham","year":"2018"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26169"},{"key":"ref49","article-title":"NAS-bench-301 and the case for surrogate benchmarks for neural architecture search","author":"Siems","year":"2020"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01082"},{"key":"ref51","first-page":"6377","article-title":"Pruning neural networks without any data by iteratively conserving synaptic flow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Tanaka","year":"2020"},{"key":"ref52","article-title":"Fisher-guided block substitution for network compression on a budget","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Turner","year":"2020"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01298"},{"key":"ref54","article-title":"Picking winning tickets before training by preserving gradient flow","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang","year":"2020"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00635"},{"key":"ref56","article-title":"Rethinking architecture selection in differentiable NAS","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang","year":"2021"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_39"},{"key":"ref58","first-page":"28904","article-title":"Stronger nas with weaker predictors","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wu","year":"2021"},{"key":"ref59","article-title":"Zero-cost proxies meet differentiable architecture search","author":"Xiang","year":"2021"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01159"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.154"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01162"},{"key":"ref63","article-title":"SNAS: Stochastic neural architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xie","year":"2019"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00675"},{"key":"ref65","article-title":"PC-DARTS: Partial channel connections for memory-efficient differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2020"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00190"},{"key":"ref67","first-page":"7105","article-title":"NAS-bench-101 Towards reproducible neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ying","year":"2019"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_41"},{"key":"ref69","article-title":"How to train your super-Net: An analysis of training heuristics in weight-sharing NAS","author":"Yu","year":"2020"},{"key":"ref70","article-title":"Towards automated deep learning: Efficient joint neural architecture and hyperparameter search","volume-title":"Proc. Int Conf. Mach. Learn. Workshop AutoML","author":"Zela","year":"2018"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00783"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01157"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01177"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01161"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3065138"},{"key":"ref77","first-page":"1","article-title":"Dynamic distribution pruning for efficient network architecture search","author":"Zheng","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00139"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01137"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.544"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01141"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01062"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01177"},{"key":"ref84","article-title":"Neural architecture search with reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zoph","year":"2017"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11372200\/11230099.pdf?arnumber=11230099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:05:35Z","timestamp":1770671135000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11230099\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":85,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3629288","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}