{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:15:24Z","timestamp":1760890524169,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2021YFB2501800"],"award-info":[{"award-number":["2021YFB2501800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Tianjin Technology Innovation Guide Special","award":["21YDTPJC00130"],"award-info":[{"award-number":["21YDTPJC00130"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s13042-023-01908-4","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T15:23:28Z","timestamp":1688570608000},"page":"267-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-scale adaptive networks for efficient inference"],"prefix":"10.1007","volume":"15","author":[{"given":"Linfeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixing","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maowei","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,5]]},"reference":[{"key":"1908_CR1","unstructured":"AAAI Press, pp 7945\u20137952. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16969"},{"key":"1908_CR2","doi-asserted-by":"publisher","unstructured":"Chen P, Liu S, Zhao H et al (2021b) Distilling knowledge via knowledge review. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021 Computer Vision Foundation \/ IEEE, pp 5008\u20135017. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00497, https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.html","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"1908_CR3","doi-asserted-by":"crossref","unstructured":"Chen D, Mei J, Zhang Y et al (2021a) Cross-layer distillation with semantic calibration. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 AAAI Press, pp 7028\u20137036. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16865","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"1908_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3133262","author":"G Du","year":"2021","unstructured":"Du G, Zhang J, Jiang M et al (2021) Graph-based class-imbalance learning with label enhancement. IEEE Trans Neural Netw Learn Syst Early Access. https:\/\/doi.org\/10.1109\/TNNLS.2021.3133262","journal-title":"IEEE Trans Neural Netw Learn Syst Early Access"},{"key":"1908_CR5","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 IEEE Computer Society, pp 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1908_CR6","unstructured":"Hinton GE, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. CoRR. arXiv:1503.02531"},{"key":"1908_CR7","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, (2021) Computer Vision Foundation \/ IEEE, pp 13,713\u201313,722, https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"1908_CR8","unstructured":"Howard AG, Zhu M, Chen B et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR. arXiv: 1704.04861"},{"key":"1908_CR9","doi-asserted-by":"publisher","unstructured":"Howard A, Pang R, Adam H et al (2019) Searching for mobilenetv 3 In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 2, (2019) IEEE, pp 1314\u20131324. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1908_CR10","unstructured":"Huang G, Chen D, Li T et al (2018) Multi-scale dense networks for resource efficient image classification. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=Hk2aImxAb"},{"key":"1908_CR11","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18\u201322, (2018) Computer Vision Foundation\/IEEE Computer Society, pp 7132\u20137141. https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1908_CR12","doi-asserted-by":"publisher","unstructured":"IEEE Computer Society, pp 936\u2013944. https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"1908_CR13","doi-asserted-by":"crossref","unstructured":"Ji M, Heo B, Park S (2021a) Show, attend and distill: Knowledge distillation via attention-based feature matching. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2\u20139","DOI":"10.1609\/aaai.v35i9.16969"},{"key":"1908_CR14","doi-asserted-by":"crossref","unstructured":"Ji M, Shin S, Hwang S et al (2021b) Refine myself by teaching myself: Feature refinement via self-knowledge distillation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19\u201325, (2021) Computer Vision Foundation\/IEEE, pp 10,664\u201310,673. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Ji_Refine_Myself_by_Teaching_Myself_Feature_Refinement_via_Self-Knowledge_Distillation_CVPR_2021_paper.html","DOI":"10.1109\/CVPR46437.2021.01052"},{"issue":"5","key":"1908_CR15","doi-asserted-by":"publisher","first-page":"658","DOI":"10.4218\/etrij.2020-0112","volume":"42","author":"C Lee","year":"2020","unstructured":"Lee C, Hong S, Hong S et al (2020) Performance analysis of local exit for distributed deep neural networks over cloud and edge computing. ETRI J 42(5):658\u2013668","journal-title":"ETRI J"},{"key":"1908_CR16","doi-asserted-by":"crossref","unstructured":"Lin T, Doll\u00e1r P, Girshick RB et al (2017) Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326,","DOI":"10.1109\/CVPR.2017.106"},{"key":"1908_CR17","doi-asserted-by":"crossref","unstructured":"Li L, Su W, Liu F et al (2023) Knowledge fusion distillation: improving distillation with multi-scale attention mechanisms. Neural Process Lett 1\u201316","DOI":"10.1007\/s11063-022-11132-w"},{"issue":"108","key":"1908_CR18","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.knosys.2022.108171","volume":"241","author":"Y Liu","year":"2022","unstructured":"Liu Y, Ng MK (2022) Deep neural network compression by tucker decomposition with nonlinear response. Knowl Based Syst 241(108):171. https:\/\/doi.org\/10.1016\/j.knosys.2022.108171","journal-title":"Knowl Based Syst"},{"key":"1908_CR19","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536. https:\/\/www.nature.com\/articles\/323533a0","DOI":"10.1038\/323533a0"},{"issue":"2","key":"1908_CR20","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s13042-021-01411-8","volume":"13","author":"M Shao","year":"2022","unstructured":"Shao M, Dai J, Wang R et al (2022) CSHE: network pruning by using cluster similarity and matrix eigenvalues. Int J Mach Learn Cybern 13(2):371\u2013382. https:\/\/doi.org\/10.1007\/s13042-021-01411-8","journal-title":"Int J Mach Learn Cybern"},{"key":"1908_CR21","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y (eds) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference Track Proceedings. arXiv: http:\/\/arxiv.org\/abs\/1409.1556"},{"issue":"8","key":"1908_CR22","doi-asserted-by":"publisher","first-page":"6125","DOI":"10.1007\/s10462-022-10141-4","volume":"55","author":"W Su","year":"2022","unstructured":"Su W, Li L, Liu F et al (2022) AI on the edge: a comprehensive review. Artif Intell Rev 55(8):6125\u20136183. https:\/\/doi.org\/10.1007\/s10462-022-10141-4","journal-title":"Artif Intell Rev"},{"key":"1908_CR23","doi-asserted-by":"publisher","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13\u201319, (2020) Computer Vision Foundation \/ IEEE, pp 10,778\u201310,787. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079, https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Tan_EfficientDet_Scalable_and_Efficient_Object_Detection_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1908_CR24","doi-asserted-by":"publisher","unstructured":"Teerapittayanon S, McDanel B, Kung HT (2016) Branchynet: fast inference via early exiting from deep neural networks. In: 23rd International Conference on Pattern Recognition, ICPR 2016, Canc\u00fan, Mexico, December 4\u20138, 2016. IEEE, pp 2464\u20132469. https:\/\/doi.org\/10.1109\/ICPR.2016.7900006","DOI":"10.1109\/ICPR.2016.7900006"},{"issue":"5","key":"1908_CR25","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1007\/s13042-022-01737-x","volume":"14","author":"Z Wang","year":"2023","unstructured":"Wang Z, Zhu H, Liu M et al (2023) Tagnet: a tiny answer-guided network for conversational question generation. Int J Mach Learn Cybern 14(5):1921\u20131932. https:\/\/doi.org\/10.1007\/s13042-022-01737-x","journal-title":"Int J Mach Learn Cybern"},{"key":"1908_CR26","doi-asserted-by":"publisher","unstructured":"Wang F, Jiang M, Qian C et al (2017) Residual attention network for image classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326,(2017) IEEE Computer Society, pp 6450\u20136458. https:\/\/doi.org\/10.1109\/CVPR.2017.683","DOI":"10.1109\/CVPR.2017.683"},{"key":"1908_CR27","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee J et\u00a0al (2018) CBAM: convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, et\u00a0al (eds) Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part VII, Lecture Notes in Computer Science, vol 11211. Springer, pp 3\u201319. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1908_CR28","doi-asserted-by":"publisher","unstructured":"Xie S, Girshick RB, Doll\u00e1r P et al (2017) Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, (2017) IEEE Computer Society, pp 5987\u20135995. https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"1908_CR29","unstructured":"Yang J, Mart\u00ednez B, Bulat A et al (2020) Knowledge distillation via adaptive instance normalization. CoRR. arXiv: 2003.04289"},{"issue":"9","key":"1908_CR30","doi-asserted-by":"publisher","first-page":"5700","DOI":"10.1109\/TPAMI.2021.3084839","volume":"44","author":"SI Young","year":"2022","unstructured":"Young SI, Wang Z, Taubman D et al (2022) Transform quantization for CNN compression. IEEE Trans Pattern Anal Mach Intell 44(9):5700\u20135714. https:\/\/doi.org\/10.1109\/TPAMI.2021.3084839","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1908_CR31","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Wilson RC, Hancock ER, Smith WAP (eds) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19\u201322, (2016) BMVA Press. http:\/\/www.bmva.org\/bmvc\/2016\/papers\/paper087\/index.html","DOI":"10.5244\/C.30.87"},{"key":"1908_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3067100","author":"L Zhang","year":"2021","unstructured":"Zhang L, Bao C, Ma K (2021) Self-distillation: towards efficient and compact neural networks. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3067100","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1908_CR33","doi-asserted-by":"publisher","unstructured":"Zhang L, Song J, Gao A et al (2019a) Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 2, (2019) IEEE, pp 3712\u20133721. https:\/\/doi.org\/10.1109\/ICCV.2019.00381","DOI":"10.1109\/ICCV.2019.00381"},{"key":"1908_CR34","unstructured":"Zhang L, Tan Z, Song J et al (2019b) SCAN: a scalable neural networks framework towards compact and efficient models. In: Wallach HM, Larochelle H, Beygelzimer A et\u00a0al (eds) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8\u201314, 2019, Vancouver, BC, Canada, pp 4029\u20134038. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/934b535800b1cba8f96a5d72f72f1611-Abstract.html"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01908-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01908-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01908-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T16:21:56Z","timestamp":1705076516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01908-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,5]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1908"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01908-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2023,7,5]]},"assertion":[{"value":"24 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}