{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:49:38Z","timestamp":1781884178479,"version":"3.54.5"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61673396"],"award-info":[{"award-number":["61673396"]}],"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":["61976245"],"award-info":[{"award-number":["61976245"]}],"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":["61772344"],"award-info":[{"award-number":["61772344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s13042-021-01411-8","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T18:02:57Z","timestamp":1631556177000},"page":"371-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["CSHE: network pruning by using cluster similarity and matrix eigenvalues"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7323-5896","authenticated-orcid":false,"given":"Mingwen","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhui","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiandong","family":"Kuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wangmeng","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"1411_CR1","doi-asserted-by":"crossref","unstructured":"Lin J, Pang Y, Xia Y et\u00a0al (2020) TuiGAN: learning versatile image-to-image translation with two unpaired images. In: European Conference on computer vision. Springe, pp 18\u201335","DOI":"10.1007\/978-3-030-58548-8_2"},{"key":"1411_CR2","doi-asserted-by":"crossref","unstructured":"Li X, Zhang S, Hu J et\u00a0al (2021) Image-to-image translation via hierarchical style disentanglement. arXiv preprint arXiv:2103.01456","DOI":"10.1109\/CVPR46437.2021.00853"},{"key":"1411_CR3","doi-asserted-by":"crossref","unstructured":"Yi R, Xia M, Liu YJ et al (2020) Line drawings for face portraits from photos using global and local structure based GANs. IEEE Trans Pattern Anal Mach Intell 43(10):3462\u20133475","DOI":"10.1109\/TPAMI.2020.2987931"},{"key":"1411_CR4","doi-asserted-by":"crossref","unstructured":"Zhu C, Chen F, Ahmed U et\u00a0al (2021) Semantic relation reasoning for shot-stable few-shot object detection. arXiv preprint arXiv:2103.01903","DOI":"10.1109\/CVPR46437.2021.00867"},{"key":"1411_CR5","doi-asserted-by":"crossref","unstructured":"Yang C, Wu Z, Zhou B et\u00a0al (2021) Instance localization for self-supervised detection pretraining. arXiv preprint arXiv:2102.08318","DOI":"10.1109\/CVPR46437.2021.00398"},{"key":"1411_CR6","unstructured":"Bronskill J, Gordon J, Requeima J et\u00a0al (2020) Tasknorm: rethinking batch normalization for meta-learning. In: International Conference on machine learning. PMLR, pp 1153\u20131164"},{"key":"1411_CR7","unstructured":"Yang S, Liu L, Xu M (2021) Free lunch for few-shot learning: distribution calibration. arXiv preprint arXiv:2101.06395"},{"key":"1411_CR8","unstructured":"Ding X, Ding G, Guo Y et\u00a0al (2019) Approximated oracle filter pruning for destructive cnn width optimization. arXiv preprint arXiv:1905.04748"},{"key":"1411_CR9","unstructured":"Calvi GG, Moniri A, Mahfouzet M et al (2019) Compression and interpretability of deep neural networks via tucker tensor layer: from first principles to tensor valued back-propagation. arXiv preprint arXiv:1903.06133"},{"key":"1411_CR10","unstructured":"Shen Z, Savvides M (2020) Meal v2: boosting vanilla resnet-50 to 80%+ top-1 accuracy on imagenet without tricks. arXiv preprint arXiv:2009.08453"},{"key":"1411_CR11","unstructured":"Howard AG, Zhu M, Chen B et\u00a0al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"1411_CR12","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1411_CR13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252","journal-title":"Int J Comput Vis"},{"key":"1411_CR14","doi-asserted-by":"crossref","unstructured":"He Y, Liu P, Wang Z et al (2019) Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4340\u20134349","DOI":"10.1109\/CVPR.2019.00447"},{"key":"1411_CR15","doi-asserted-by":"crossref","unstructured":"Liu Z, Li J, Shen Z et\u00a0al (2017) Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on computer vision, pp 2736\u20132744","DOI":"10.1109\/ICCV.2017.298"},{"key":"1411_CR16","doi-asserted-by":"crossref","unstructured":"Zhao C, Ni B, Zhang J et\u00a0al (2019) Variational convolutional neural network pruning. In: Proceedings of the IEEE Conference on Computer vision and pattern recognition, pp 2780\u20132789","DOI":"10.1109\/CVPR.2019.00289"},{"key":"1411_CR17","unstructured":"Li H, Kadav A, Durdanovic I (2016) Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710"},{"key":"1411_CR18","doi-asserted-by":"crossref","unstructured":"Guo J, Ouyang W, Xu D (2020) Channel pruning guided by classification loss and feature importance. arXiv preprint arXiv:2003.06757","DOI":"10.1609\/aaai.v34i07.6720"},{"key":"1411_CR19","doi-asserted-by":"crossref","unstructured":"Lin S, Ji R, Yan C et\u00a0al (2019) Towards optimal structured cnn pruning via generative adversarial learning. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 2790\u20132799","DOI":"10.1109\/CVPR.2019.00290"},{"key":"1411_CR20","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang N (2018) Data-driven sparse structure selection for deep neural networks. In: Proceedings of the European Conference on computer vision (ECCV), pp 304\u2013320","DOI":"10.1007\/978-3-030-01270-0_19"},{"issue":"10","key":"1411_CR21","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.3390\/s20102891","volume":"20","author":"H Pan","year":"2020","unstructured":"Pan H, Badawi D, Cetin AE (2020) Computationally efficient wildfire detection method using a deep convolutional network pruned via Fourier analysis. Sensors 20(10):2891","journal-title":"Sensors"},{"key":"1411_CR22","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"1411_CR23","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400"},{"key":"1411_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1411_CR25","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M et\u00a0al (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1411_CR26","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q (2020) GhostNet: more features from cheap operations. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"1411_CR27","doi-asserted-by":"crossref","unstructured":"Zhou D, Zhou X, Zhang W et al (2020) EcoNAS: finding proxies for economical neural architecture search. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 11396\u201311404","DOI":"10.1109\/CVPR42600.2020.01141"},{"key":"1411_CR28","doi-asserted-by":"crossref","unstructured":"Gao Y, Bai H, Jie Z (2020) Mtl-nas: task-agnostic neural architecture search towards general-purpose multi-task learning. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 11543\u201311552","DOI":"10.1109\/CVPR42600.2020.01156"},{"key":"1411_CR29","doi-asserted-by":"crossref","unstructured":"Li X, Lin C, Li C (2020) Improving one-shot nas by suppressing the posterior fading. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 13836\u201313845","DOI":"10.1109\/CVPR42600.2020.01385"},{"key":"1411_CR30","unstructured":"Park J, Li S, Wen W et\u00a0al (2016) Faster cnns with direct sparse convolutions and guided pruning. arXiv preprint arXiv:1608.01409"},{"key":"1411_CR31","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1145\/3007787.3001163","volume":"44","author":"S Han","year":"2016","unstructured":"Han S, Liu X, Mao H (2016) EIE: efficient inference engine on compressed deep neural network. ACM SIGARCH Comput Arch News 44:243\u2013254","journal-title":"ACM SIGARCH Comput Arch News"},{"key":"1411_CR32","doi-asserted-by":"crossref","DOI":"10.56021\/9781421407944","volume-title":"Matrix computations","author":"GH Golub","year":"2013","unstructured":"Golub GH, Van LCF (2013) Matrix computations, vol 3. JHU Press, Baltimore"},{"key":"1411_CR33","first-page":"881","volume":"2002","author":"T Kanungo","year":"1980","unstructured":"Kanungo T, Mount DM, Netanyahu NS et al (1980) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 2002:881\u2013892","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1411_CR34","unstructured":"Wang D, Zhou L, Zhang X et\u00a0al (2018) Exploring linear relationship in feature map subspace for convnets compression. arXiv preprint arXiv:1803.05729"},{"key":"1411_CR35","doi-asserted-by":"crossref","unstructured":"Klema V, Laub A (1980) The singular value decomposition: Its computation and some applications. IEEE Trans Autom Control 25(2):164\u2013176","DOI":"10.1109\/TAC.1980.1102314"},{"key":"1411_CR36","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images"},{"key":"1411_CR37","unstructured":"Paszke A, Gross S, Chintala S et\u00a0al (2017) Automatic differentiation in pytorch"},{"key":"1411_CR38","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1411_CR39","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van DM et\u00a0al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1411_CR40","unstructured":"Ayinde BO, Zurada JM (2018) Building efficient convnets using redundant feature pruning. arXiv preprint arXiv:1802.07653"},{"key":"1411_CR41","doi-asserted-by":"crossref","unstructured":"Molchanov P, Mallya A, Tyree S et al (2019) Importance estimation for neural network pruning. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 11264\u201311272","DOI":"10.1109\/CVPR.2019.01152"},{"key":"1411_CR42","doi-asserted-by":"crossref","unstructured":"Dong X, Huang J, Yang Y, Yan S (2017) More is less: a more complicated network with less inference complexity. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 5840\u20135848","DOI":"10.1109\/CVPR.2017.205"},{"key":"1411_CR43","doi-asserted-by":"crossref","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on computer vision, pp 1389\u20131397","DOI":"10.1109\/ICCV.2017.155"},{"key":"1411_CR44","doi-asserted-by":"crossref","unstructured":"He Y, Kang G, Dong X, Fu Y, Yang Y (2018) Soft filter pruning for accelerating deep convolutional neural networks. arXiv preprint arXiv:1808.06866","DOI":"10.24963\/ijcai.2018\/309"},{"key":"1411_CR45","doi-asserted-by":"crossref","unstructured":"Luo JH, Wu J, Lin W (2017) Thinet: a filter level pruning method for deep neural network compression. In: Proceedings of the IEEE International Conference on computer vision, pp 5058\u20135066","DOI":"10.1109\/ICCV.2017.541"},{"key":"1411_CR46","doi-asserted-by":"crossref","unstructured":"Lin M, Ji R, Wang Y et\u00a0al (2020) Hrank: filter pruning using high-rank feature ma. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 1529\u20131538","DOI":"10.1109\/CVPR42600.2020.00160"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01411-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01411-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01411-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T03:10:08Z","timestamp":1673233808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01411-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["1411"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01411-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,13]]},"assertion":[{"value":"16 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}