{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:44:33Z","timestamp":1762325073842},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s11432-020-3112-8","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T04:19:19Z","timestamp":1628741959000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["EAT-NAS: elastic architecture transfer for accelerating large-scale neural architecture search"],"prefix":"10.1007","volume":"64","author":[{"given":"Jiemin","family":"Fang","sequence":"first","affiliation":[]},{"given":"Yukang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xinbang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Gaofeng","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Wenyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xinggang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"3112_CR1","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016","DOI":"10.1109\/CVPR.2016.308"},{"key":"3112_CR2","doi-asserted-by":"crossref","unstructured":"He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"3112_CR3","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, et al. Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018","DOI":"10.1109\/CVPR.2018.00474"},{"key":"3112_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L C Chen","year":"2018","unstructured":"Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell, 2018, 40: 834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3112_CR5","unstructured":"Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. 2017. ArXiv:1706.05587"},{"key":"3112_CR6","doi-asserted-by":"crossref","unstructured":"Huang Z L, Wang X G, Huang L C, et al. CCNet: criss-cross attention for semantic segmentation. In: Proceedings of International Conference on Computer Vision, 2019","DOI":"10.1109\/ICCV.2019.00069"},{"key":"3112_CR7","doi-asserted-by":"publisher","unstructured":"Huang Z L, Wang X G, Wei Y C, et al. CCNet: criss-cross attention for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2020. doi: https:\/\/doi.org\/10.1109\/TPAMI.2020.3007032","DOI":"10.1109\/TPAMI.2020.3007032"},{"key":"3112_CR8","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017, 39: 1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3112_CR9","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, 2016","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3112_CR10","doi-asserted-by":"crossref","unstructured":"Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. In: Proceedings of International Conference on Computer Vision, 2017","DOI":"10.1109\/ICCV.2017.324"},{"key":"3112_CR11","doi-asserted-by":"publisher","first-page":"2503","DOI":"10.1109\/TCSVT.2019.2925844","volume":"30","author":"P Yi","year":"2020","unstructured":"Yi P, Wang Z Y, Jiang K, et al. Multi-temporal ultra dense memory network for video super-resolution. IEEE Trans Circ Syst Video Technol, 2020, 30: 2503\u20132516","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"3112_CR12","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, et al. Learning transferable architectures for scalable image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018","DOI":"10.1109\/CVPR.2018.00907"},{"key":"3112_CR13","doi-asserted-by":"crossref","unstructured":"Real E, Aggarwal A, Huang Y, et al. Regularized evolution for image classifier architecture search. In: Proceedings of AAAI Conference on Artificial Intelligence, 2019","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"3112_CR14","unstructured":"Pham H, Guan M Y, Zoph B, et al. Efficient neural architecture search via parameter sharing. In: Proceedings of International Conference on Machine Learning, 2018"},{"key":"3112_CR15","unstructured":"Zoph B, Le Q V. Neural architecture search with reinforcement learning. In: Proceedings of International Conference on Learning Representations, 2017"},{"key":"3112_CR16","unstructured":"Krizhevsky A, Hinton G. Learning Multiple Layers of Features From Tiny Images. Technical Report, 2009"},{"key":"3112_CR17","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3112_CR18","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. ArXiv:1409.1556"},{"key":"3112_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3112_CR20","doi-asserted-by":"crossref","unstructured":"Liu C X, Zoph B, Neumann M, et al. Progressive neural architecture search. In: Proceedings of European Conference on Computer Vision, 2018","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"3112_CR21","doi-asserted-by":"crossref","unstructured":"Tommasi T, Patricia N, Caputo B, et al. A deeper look at dataset bias. In: Proceedings of Domain Adaptation in Computer Vision Applications, 2017","DOI":"10.1007\/978-3-319-58347-1_2"},{"key":"3112_CR22","doi-asserted-by":"crossref","unstructured":"Tan M X, Chen B, Pang R M, et al. Mnasnet: platform-aware neural architecture search for mobile. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019","DOI":"10.1109\/CVPR.2019.00293"},{"key":"3112_CR23","doi-asserted-by":"crossref","unstructured":"Zhong Z, Yan J J, Wu W, et al. Practical block-wise neural network architecture generation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018","DOI":"10.1109\/CVPR.2018.00257"},{"key":"3112_CR24","doi-asserted-by":"crossref","unstructured":"Miikkulainen R, Liang J, Meyerson E, et al. Evolving deep neural networks. In: Proceedings of Artificial Intelligence in the Age of Neural Networks and Brain Computing, 2019","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"3112_CR25","doi-asserted-by":"crossref","unstructured":"Lu Z C, Whalen I, Boddeti V, et al. NSGA-Net: a multi-objective genetic algorithm for neural architecture search. 2018. ArXiv:1810.03522","DOI":"10.1145\/3321707.3321729"},{"key":"3112_CR26","unstructured":"Liu H, Simonyan K, Yang Y. DARTS: differentiable architecture search. In: Proceedings of International Conference on Learning Representations, 2019"},{"key":"3112_CR27","unstructured":"Zhang X B, Huang Z H, Wang N Y. You only search once: single shot neural architecture search via direct sparse optimization. 2018. ArXiv:1811.01567"},{"key":"3112_CR28","unstructured":"Cai H, Zhu L G, Han S. ProxylessNAS: direct neural architecture search on target task and hardware. In: Proceedings of International Conference on Learning Representations, 2019"},{"key":"3112_CR29","doi-asserted-by":"crossref","unstructured":"Fang J M, Sun Y Z, Zhang Q, et al. Densely connected search space for more flexible neural architecture search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020","DOI":"10.1109\/CVPR42600.2020.01064"},{"key":"3112_CR30","unstructured":"Fang J M, Sun Y Z, Peng K, et al. Fast neural network adaptation via parameter remapping and architecture search. In: Proceedings of International Conference on Learning Representations, 2020"},{"key":"3112_CR31","doi-asserted-by":"crossref","unstructured":"Dong X Y, Yang Y. Searching for a robust neural architecture in four GPU hours. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019","DOI":"10.1109\/CVPR.2019.00186"},{"key":"3112_CR32","unstructured":"Mei J R, Li Y W, Lian X C, et al. Atomnas: fine-grained end-to-end neural architecture search. In: Proceedings of International Conference on Learning Representations, 2020"},{"key":"3112_CR33","doi-asserted-by":"crossref","unstructured":"Wu B C, Dai X L, Zhang P Z, et al. FBNet: hardware-aware efficient convnet design via differentiable neural architecture search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019","DOI":"10.1109\/CVPR.2019.01099"},{"key":"3112_CR34","unstructured":"Chang J L, Zhang X B, Guo Y W, et al. Data: differentiable architecture approximation. In: Proceedings of Conference on Neural Information Processing Systems, 2019"},{"key":"3112_CR35","unstructured":"Wong C, Houlsby N, Lu Y F, et al. Transfer learning with neural automl. In: Proceedings of Conference on Neural Information Processing Systems, 2018"},{"key":"3112_CR36","doi-asserted-by":"crossref","unstructured":"Deb K. Multi-objective optimization. In: Proceedings of Search Methodologies, 2014","DOI":"10.1007\/978-1-4614-6940-7_15"},{"key":"3112_CR37","first-page":"69","volume":"1","author":"D E Goldberg","year":"1991","unstructured":"Goldberg D E, Deb K. A comparative analysis of selection schemes used in genetic algorithms. Found Genetic Algorithms, 1991, 1: 69\u201393","journal-title":"Found Genetic Algorithms"},{"key":"3112_CR38","unstructured":"Liu H X, Simonyan K, Vinyals O, et al. Hierarchical representations for efficient architecture search. In: Proceedings of International Conference on Learning Representations, 2018"},{"key":"3112_CR39","doi-asserted-by":"crossref","unstructured":"Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017","DOI":"10.1109\/CVPR.2017.195"},{"key":"3112_CR40","unstructured":"Chen T, Goodfellow I J, Shlens J. Net2Net: accelerating learning via knowledge transfer. In: Proceedings of International Conference on Learning Representations, 2016"},{"key":"3112_CR41","unstructured":"Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts. 2016. ArXiv:1608.03983"},{"key":"3112_CR42","unstructured":"DeVries T, Taylor G W. Improved regularization of convolutional neural networks with cutout. 2017. ArXiv:1708.04552"},{"key":"3112_CR43","unstructured":"Howard A G, Zhu M L, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. 2017. ArXiv:1704.04861"},{"key":"3112_CR44","doi-asserted-by":"crossref","unstructured":"Zhang X Y, Zhou X Y, Lin M X, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018","DOI":"10.1109\/CVPR.2018.00716"},{"key":"3112_CR45","doi-asserted-by":"crossref","unstructured":"Chen X, Xie L X, Wu J, et al. Progressive differentiable architecture search: bridging the depth GAP between search and evaluation. In: Proceedings of International Conference on Computer Vision, 2019","DOI":"10.1109\/ICCV.2019.00138"},{"key":"3112_CR46","unstructured":"Xu Y H, Xie L X, Zhang X P, et al. PC-DARTS: partial channel connections for memory-efficient architecture search. In: Proceedings of International Conference on Learning Representations, 2020"},{"key":"3112_CR47","unstructured":"Xie S R, Zheng H H, Liu C X, et al. SNAS: stochastic neural architecture search. In: Proceedings of International Conference on Learning Representations, 2019"},{"key":"3112_CR48","unstructured":"Real E, Moore S, Selle A, et al. Large-scale evolution of image classifiers. In: Proceedings of International Conference on Machine Learning, 2017"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-3112-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-020-3112-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-3112-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T20:17:31Z","timestamp":1666297051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-020-3112-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,6]]},"references-count":48,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["3112"],"URL":"https:\/\/doi.org\/10.1007\/s11432-020-3112-8","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,6]]},"assertion":[{"value":"28 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"192106"}}