{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:21:41Z","timestamp":1743826901187,"version":"3.40.3"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0107400"],"award-info":[{"award-number":["2020AAA0107400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20222"],"award-info":[{"award-number":["U20A20222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LR19F020004"],"award-info":[{"award-number":["LR19F020004"]}]},{"name":"key scientific technological innovation research project by Ministry of Education"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,10,1]]},"DOI":"10.1109\/tpami.2021.3084680","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T20:16:38Z","timestamp":1622232998000},"page":"6011-6023","source":"Crossref","is-referenced-by-count":2,"title":["CoDiNet: Path Distribution Modeling With Consistency and Diversity for Dynamic Routing"],"prefix":"10.1109","volume":"44","author":[{"given":"Huanyu","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"given":"Zequn","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"given":"Songyuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3023-1662","authenticated-orcid":false,"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_25"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00919"},{"key":"ref4","first-page":"8817","article-title":"Dynamic capacity networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Almahairi"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref6","first-page":"1058","article-title":"Regularization of neural networks using dropConnect","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wan"},{"key":"ref7","article-title":"Dropout: A simple way to prevent neural networks from overfitting","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref8","first-page":"550","article-title":"Residual networks behave like ensembles of relatively shallow networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Veit"},{"key":"ref9","first-page":"1","article-title":"Slimmable neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yu"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00239"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00189"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218645"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_31"},{"key":"ref14","first-page":"1","article-title":"Multi-scale dense networks for resource efficient image classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Huang"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00244"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58539-6_9"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2975987"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref20","first-page":"529","article-title":"Boosting on a budget: Sampling for feature-efficient prediction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Reyzin"},{"key":"ref21","first-page":"279","article-title":"Efficient feature group sequencing for anytime linear prediction","volume-title":"Proc. 32nd Conf. Uncer. Artif. Intell.","author":"Hu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"article-title":"Adaptive computation time for recurrent neural networks","year":"2016","author":"Graves","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.194"},{"key":"ref25","first-page":"1","article-title":"Multi-scale dense networks for resource efficient image classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Huang"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00216"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00198"},{"key":"ref29","first-page":"1","article-title":"Dynamic channel pruning: Feature boosting and suppression","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gao"},{"key":"ref30","first-page":"8080","article-title":"Hydranets: Specialized dynamic architectures for efficient inference","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Teja Mullapudi"},{"article-title":"TimeGate: Conditional gating of segments in long-range activities","year":"2020","author":"Hussein","key":"ref31"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_6"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00011"},{"key":"ref34","first-page":"1","article-title":"Distilling the knowledge in a neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hinton"},{"key":"ref35","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2789925"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref38","first-page":"1","article-title":"Training CNNs with low-rank filters for efficient image classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ioannou"},{"key":"ref39","first-page":"1","article-title":"Convolutional neural networks with low-rank regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"McIntosh"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.88"},{"key":"ref41","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Han"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.521"},{"key":"ref43","first-page":"1","article-title":"Model compression via distillation and quantization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Polino"},{"key":"ref44","first-page":"1","article-title":"Pruning filters for efficient convnets","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref47","first-page":"1","article-title":"Learning structured sparsity in deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wen"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00291"},{"key":"ref49","first-page":"1","article-title":"PerforatedCNNs: Acceleration through elimination of redundant convolutions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Figurnov"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref51","first-page":"1","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai"},{"key":"ref52","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"ref54","first-page":"1","article-title":"Categorical reparameterization with gumbel-softmax","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jang"},{"key":"ref55","article-title":"Learning multiple layers of features from tiny images","volume-title":"Tech. Rep. TR-2009","author":"Krizhevsky","year":"2009"},{"key":"ref56","first-page":"1","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Netzer"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref58","first-page":"1","article-title":"IamNN: Iterative and adaptive mobile neural network for efficient image classification","volume-title":"Proc. Int. Conf. Learn. Representations Workshop","author":"Leroux"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2019.2933233"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2979669"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00093"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.205"},{"first-page":"5147","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Guo","key":"ref63"},{"first-page":"2366","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Yang","key":"ref64"},{"volume-title":"IEEE Trans. Emer. Topi. Comput.","year":"2021","author":"Xia","key":"ref65"},{"issue":"10","key":"ref66","first-page":"2291","volume-title":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"41","author":"Rao","year":"2019"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"first-page":"1294","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"Chen","key":"ref68"},{"article-title":"DARTS+: Improved differentiable architecture search with early stopping","year":"2019","author":"Liang","key":"ref69"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9893034\/09444192.pdf?arnumber=9444192","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:44:04Z","timestamp":1743795844000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9444192\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":69,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3084680","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2022,10,1]]}}}