{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T23:05:36Z","timestamp":1771542336268,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11432-022-3951-y","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T01:27:25Z","timestamp":1734917245000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Channel pruning on frequency response"],"prefix":"10.1007","volume":"68","author":[{"given":"Hang","family":"Lin","sequence":"first","affiliation":[]},{"given":"Yifan","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Bie","sequence":"additional","affiliation":[]},{"given":"Chenggang","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Xibin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"3951_CR1","first-page":"2704","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"B Jacob","year":"2018","unstructured":"Jacob B, Kligys S, Chen B, et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 2704\u20132713"},{"key":"3951_CR2","first-page":"525","volume-title":"Proceedings of European Conference on Computer Vision","author":"M Rastegari","year":"2016","unstructured":"Rastegari M, Ordonez V, Redmon J, et al. XNOR-Net: ImageNet classification using binary convolutional neural networks. In: Proceedings of European Conference on Computer Vision, 2016. 525\u2013542"},{"key":"3951_CR3","first-page":"28","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"S Han","year":"2015","unstructured":"Han S, Pool J, Tran J, et al. Learning both weights and connections for efficient neural network. In: Proceedings of Advances in Neural Information Processing Systems, 2015. 28"},{"key":"3951_CR4","first-page":"2790","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"S Lin","year":"2019","unstructured":"Lin S, Ji R, Yan C, et al. Towards optimal structured CNN pruning via generative adversarial learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 2790\u20132799"},{"key":"3951_CR5","first-page":"1529","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"M Lin","year":"2020","unstructured":"Lin M, Ji R, Wang Y, et al. HRank: filter pruning using high-rank feature map. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 1529\u20131538"},{"key":"3951_CR6","first-page":"1","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"S Hanson","year":"1988","unstructured":"Hanson S, Pratt L. Comparing biases for minimal network construction with back-propagation. In: Proceedings of Advances in Neural Information Processing Systems, 1988. 1"},{"key":"3951_CR7","first-page":"2","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Denker J, Solla S. Optimal brain damage. In: Proceedings of Advances in Neural Information Processing Systems, 1989. 2"},{"key":"3951_CR8","first-page":"5","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"B Hassibi","year":"1992","unstructured":"Hassibi B, Stork D. Second order derivatives for network pruning: optimal brain surgeon. In: Proceedings of Advances in Neural Information Processing Systems, 1992. 5"},{"key":"3951_CR9","first-page":"639","volume-title":"Proceedings of European Conference on Computer Vision","author":"B Li","year":"2020","unstructured":"Li B, Wu B, Su J, et al. EagleEye: fast sub-net evaluation for efficient neural network pruning. In: Proceedings of European Conference on Computer Vision, 2020. 639\u2013654"},{"key":"3951_CR10","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1007\/978-3-319-71249-9_47","volume-title":"Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"H Pratt","year":"2017","unstructured":"Pratt H, Williams B, Coenen F, et al. FCNN: Fourier convolutional neural networks. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2017. 786\u2013798"},{"key":"3951_CR11","first-page":"3999","volume":"45","author":"M Lin","year":"2022","unstructured":"Lin M, Zhang Y, Li Y, et al. 1 \u00d7 N pattern for pruning convolutional neural networks. IEEE Trans Pattern Anal Mach Intell, 2022, 45: 3999\u20134008","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3951_CR12","doi-asserted-by":"publisher","first-page":"7091","DOI":"10.1109\/TNNLS.2021.3084206","volume":"33","author":"M Lin","year":"2021","unstructured":"Lin M, Cao L, Li S, et al. Filter sketch for network pruning. IEEE Trans Neural Netw Learn Syst, 2021, 33: 7091\u20137100","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3951_CR13","doi-asserted-by":"publisher","first-page":"9139","DOI":"10.1109\/TNNLS.2022.3156047","volume":"34","author":"M Lin","year":"2023","unstructured":"Lin M, Cao L, Zhang Y, et al. Pruning networks with cross-layer ranking & k-reciprocal nearest filters. IEEE Trans Neural Netw Learn Syst, 2023, 34: 9139\u20139148","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3951_CR14","first-page":"941","volume":"35","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Lin M, Lin Z, et al. Learning best combination for efficient N:M sparsity. In: Proceedings of Advances in Neural Information Processing Systems, 2022. 35: 941\u2013953","journal-title":"Proceedings of Advances in Neural Information Processing Systems"},{"key":"3951_CR15","first-page":"2029","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Z Wang","year":"2022","unstructured":"Wang Z, Li C. Channel pruning via lookahead search guided reinforcement learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022. 2029\u20132040"},{"key":"3951_CR16","first-page":"673","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"M Lin","year":"2021","unstructured":"Lin M, Ji R, Zhang Y, et al. Channel pruning via automatic structure search. In: Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence, 2021. 673\u2013679"},{"key":"3951_CR17","first-page":"7370","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"X Yu","year":"2017","unstructured":"Yu X, Liu T, Wang X, et al. On compressing deep models by low rank and sparse decomposition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2017. 7370\u20137379"},{"key":"3951_CR18","first-page":"522","volume-title":"Proceedings of European Conference on Computer Vision","author":"A H Phan","year":"2020","unstructured":"Phan A H, Sobolev K, Sozykin K, et al. Stable low-rank tensor decomposition for compression of convolutional neural network. In: Proceedings of European Conference on Computer Vision, 2020. 522\u2013539"},{"key":"3951_CR19","first-page":"4683","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Y Pan","year":"2019","unstructured":"Pan Y, Xu J, Wang M, et al. Compressing recurrent neural networks with tensor ring for action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 4683\u20134690"},{"key":"3951_CR20","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1007\/s11042-020-09276-9","volume":"80","author":"Y K Lin","year":"2021","unstructured":"Lin Y K, Wang C F, Chang C Y, et al. An efficient framework for counting pedestrians crossing a line using low-cost devices: the benefits of distilling the knowledge in a neural network. Multimed Tools Appl, 2021, 80: 4037\u20134051","journal-title":"Multimed Tools Appl"},{"key":"3951_CR21","first-page":"7021","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"L Liu","year":"2021","unstructured":"Liu L, Zhang S, Kuang Z, et al. Group fisher pruning for practical network compression. In: Proceedings of the 38th International Conference on Machine Learning, 2021. 7021\u20137032"},{"key":"3951_CR22","first-page":"248","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"J Deng","year":"2009","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. 248\u2013255"},{"key":"3951_CR23","series-title":"Technical Report","volume-title":"Learning Multiple Layers of Features From Tiny Images","author":"A Krizhevsky","year":"2009","unstructured":"Krizhevsky A, Hinton G. Learning Multiple Layers of Features From Tiny Images. Technical Report, 2009"},{"key":"3951_CR24","first-page":"5058","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"J H Luo","year":"2017","unstructured":"Luo J H, Wu J, Lin W. ThiNet: a filter level pruning method for deep neural network compression. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017. 5058\u20135066"},{"key":"3951_CR25","unstructured":"Lee N, Ajanthan T, Torr P H S. SNIP: single-shot network pruning based on connection sensitivity. 2018. ArXiv:1810.02340"},{"key":"3951_CR26","first-page":"1740","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"K Xu","year":"2020","unstructured":"Xu K, Qin M, Sun F, et al. Learning in the frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 1740\u20131749"},{"key":"3951_CR27","first-page":"31","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"L Gueguen","year":"2018","unstructured":"Gueguen L, Sergeev A, Kadlec B, et al. Faster neural networks straight from JPEG. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 31"},{"key":"3951_CR28","first-page":"8684","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"H Wang","year":"2020","unstructured":"Wang H, Wu X, Huang Z, et al. High-frequency component helps explain the generalization of convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 8684\u20138694"},{"key":"3951_CR29","first-page":"31","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"Z Liu","year":"2018","unstructured":"Liu Z, Xu J, Peng X, et al. Frequency-domain dynamic pruning for convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 31"},{"key":"3951_CR30","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1145\/2939672.2939839","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"W Chen","year":"2016","unstructured":"Chen W, Wilson J, Tyree S, et al. Compressing convolutional neural networks in the frequency domain. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 1475\u20131484"},{"key":"3951_CR31","first-page":"29","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"Y Wang","year":"2016","unstructured":"Wang Y, Xu C, You S, et al. CNNpack: packing convolutional neural networks in the frequency domain. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 29"},{"key":"3951_CR32","first-page":"28","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"O Rippel","year":"2015","unstructured":"Rippel O, Snoek J, Adams R P. Spectral representations for convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, 2015. 28"},{"key":"3951_CR33","first-page":"32","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"D Yin","year":"2019","unstructured":"Yin D, Lopes R G, Shlens J, et al. A Fourier perspective on model robustness in computer vision. In: Proceedings of Advances in Neural Information Processing Systems, 2019. 32"},{"key":"3951_CR34","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 770\u2013778"},{"key":"3951_CR35","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"H Li","year":"2017","unstructured":"Li H, Kadav A, Durdanovic I, et al. Pruning filters for efficient ConvNets. In: Proceedings of the 5th International Conference on Learning Representations, 2017"},{"key":"3951_CR36","first-page":"6438","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y C Li","year":"2021","unstructured":"Li Y C, Lin S H, Liu J Z, et al. Towards compact CNNs via collaborative compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 6438\u20136447"},{"key":"3951_CR37","first-page":"9194","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"R Yu","year":"2018","unstructured":"Yu R, Li A, Chen C F, et al. NISP: pruning networks using neuron importance score propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 9194\u20139203"},{"key":"3951_CR38","unstructured":"Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017. ArXiv:1704.04861"},{"key":"3951_CR39","first-page":"31","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"Z Zhuang","year":"2018","unstructured":"Zhuang Z, Tan M, Zhuang B, et al. Discrimination-aware channel pruning for deep neural networks. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 31"},{"key":"3951_CR40","first-page":"14913","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Z Wang","year":"2021","unstructured":"Wang Z, Li C, Wang X. Convolutional neural network pruning with structural redundancy reduction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 14913\u201314922"},{"key":"3951_CR41","first-page":"592","volume-title":"Proceedings of European Conference on Computer Vision","author":"X Ning","year":"2020","unstructured":"Ning X, Zhao T, Li W, et al. DSA: more efficient budgeted pruning via differentiable sparsity allocation. In: Proceedings of European Conference on Computer Vision, 2020. 592\u2013607"},{"key":"3951_CR42","first-page":"5122","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"M Kang","year":"2020","unstructured":"Kang M, Han B. Operation-aware soft channel pruning using differentiable masks. In: Proceedings of the 38th International Conference on Machine Learning, 2020. 5122\u20135132"},{"key":"3951_CR43","first-page":"8018","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y Li","year":"2020","unstructured":"Li Y, Gu S, Mayer C, et al. Group sparsity: the hinge between filter pruning and decomposition for network compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 8018\u20138027"},{"key":"3951_CR44","first-page":"4943","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"X Ding","year":"2019","unstructured":"Ding X, Ding G, Guo Y, et al. Centripetal SGD for pruning very deep convolutional networks with complicated structure. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 4943\u20134953"},{"key":"3951_CR45","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"H Wang","year":"2021","unstructured":"Wang H, Qin C, Zhang Y, et al. Neural pruning via growing regularization. In: Proceedings of the 9th International Conference on Learning Representations, 2021"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3951-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-022-3951-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3951-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T22:07:05Z","timestamp":1771538825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-022-3951-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3951"],"URL":"https:\/\/doi.org\/10.1007\/s11432-022-3951-y","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"17 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"112102"}}