{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:25:15Z","timestamp":1762957515080,"version":"3.40.3"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031729829"},{"type":"electronic","value":"9783031729836"}],"license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72983-6_2","type":"book-chapter","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T09:34:20Z","timestamp":1730108060000},"page":"18-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced Sparsification via\u00a0Stimulative Training"],"prefix":"10.1007","author":[{"given":"Shengji","family":"Tang","sequence":"first","affiliation":[]},{"given":"Weihao","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Hancheng","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Chong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Baopu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"key":"2_CR1","unstructured":"Aflalo, Y., Noy, A., Lin, M., Friedman, I., Zelnik, L.: Knapsack pruning with inner distillation. arXiv preprint arXiv:2002.08258 (2020)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Aghli, N., Ribeiro, E.: Combining weight pruning and knowledge distillation for CNN compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3191\u20133198 (2021)","DOI":"10.1109\/CVPRW53098.2021.00356"},{"key":"2_CR3","unstructured":"Alvarez, J.M., Salzmann, M.: Learning the number of neurons in deep networks. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a029. Curran Associates, Inc. (2016)"},{"key":"2_CR4","unstructured":"Bai, Y., Wang, H., Tao, Z., Li, K., Fu, Y.: Dual lottery ticket hypothesis. In: International Conference on Learning Representations (2021)"},{"key":"2_CR5","first-page":"129","volume":"2","author":"D Blalock","year":"2020","unstructured":"Blalock, D., Gonzalez Ortiz, J.J., Frankle, J., Guttag, J.: What is the state of neural network pruning? Proc. Mach. Learn. Syst. 2, 129\u2013146 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"2_CR6","unstructured":"Chen, K., et al.: MMDetection: open MMLab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/s40747-020-00248-y","volume":"8","author":"L Chen","year":"2021","unstructured":"Chen, L., Chen, Y., Xi, J., Le, X.: Knowledge from the original network: restore a better pruned network with knowledge distillation. Complex Intell. Syst. 8, 709\u2013718 (2021)","journal-title":"Complex Intell. Syst."},{"key":"2_CR8","unstructured":"Chen, T., Chen, X., Ma, X., Wang, Y., Wang, Z.: Coarsening the granularity: towards structurally sparse lottery tickets. In: International Conference on Machine Learning, pp. 3025\u20133039. PMLR (2022)"},{"key":"2_CR9","unstructured":"Chen, T., Cheng, Y., Gan, Z., Yuan, L., Zhang, L., Wang, Z.: Chasing sparsity in vision transformers: an end-to-end exploration. In: Advances in Neural Information Processing Systems, vol. 34, pp. 19974\u201319988 (2021)"},{"key":"2_CR10","unstructured":"Chen, T., Liang, L., Tianyu, D., Zhu, Z., Zharkov, I.: Otov2: automatic, generic, user-friendly. In: International Conference on Learning Representations (2023)"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Chin, T.W., Ding, R., Zhang, C., Marculescu, D.: Towards efficient model compression via learned global ranking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1518\u20131528 (2020)","DOI":"10.1109\/CVPR42600.2020.00159"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Ding, X., Ding, G., Han, J., Tang, S.: Auto-balanced filter pruning for efficient convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.12262"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Ding, X., et al.: ResRep: lossless CNN pruning via decoupling remembering and forgetting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4510\u20134520 (2021)","DOI":"10.1109\/ICCV48922.2021.00447"},{"key":"2_CR15","unstructured":"Dong, X., Yang, Y.: Network pruning via transformable architecture search. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Fang, G., Ma, X., Song, M., Mi, M.B., Wang, X.: DepGraph: towards any structural pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16091\u201316101 (2023)","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"2_CR17","unstructured":"Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. In: International Conference on Learning Representations (2018)"},{"key":"2_CR18","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)"},{"key":"2_CR19","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"2_CR20","unstructured":"Hassibi, B., Stork, D.: Second order derivatives for network pruning: optimal brain surgeon. In: Advances in Neural Information Processing Systems, vol. 5 (1992)"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2019)","DOI":"10.1109\/CVPR.2019.00447"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"2_CR26","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. stat 1050, 9 (2015)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Hou, Z., et al.: Chex: channel exploration for CNN model compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12287\u201312298 (2022)","DOI":"10.1109\/CVPR52688.2022.01197"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Howard, A., et\u00a0al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2_CR29","unstructured":"Hu, H., Peng, R., Tai, Y.W., Tang, C.K.: Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv:1607.03250 (2016)"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, N.: Data-driven sparse structure selection for deep neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 304\u2013320 (2018)","DOI":"10.1007\/978-3-030-01270-0_19"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Kim, K., Ji, B., Yoon, D., Hwang, S.: Self-knowledge distillation with progressive refinement of targets. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6567\u20136576 (2021)","DOI":"10.1109\/ICCV48922.2021.00650"},{"key":"2_CR32","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)"},{"key":"2_CR33","unstructured":"LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. In: Advances in Neural Information Processing Systems, vol. 2 (1989)"},{"key":"2_CR34","unstructured":"Li, H., Asim, K., Igor, D., Hanan, S., Hans, P.G.: Pruning filters for efficient convnets. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings (2017)"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Li, T., Li, J., Liu, Z., Zhang, C.: Few sample knowledge distillation for efficient network compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14639\u201314647 (2020)","DOI":"10.1109\/CVPR42600.2020.01465"},{"issue":"2","key":"2_CR36","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TNNLS.2019.2906563","volume":"31","author":"S Lin","year":"2019","unstructured":"Lin, S., Ji, R., Li, Y., Deng, C., Li, X.: Toward compact convnets via structure-sparsity regularized filter pruning. IEEE Trans. Neural Netw. Learn. Syst. 31(2), 574\u2013588 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"2_CR38","unstructured":"Liu, B., Wang, M., Foroosh, H., Tappen, M., Pensky, M.: Sparse convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 806\u2013814 (2015)"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: MetaPruning: meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3296\u20133305 (2019)","DOI":"10.1109\/ICCV.2019.00339"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736\u20132744 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"2_CR42","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv:1711.05101 (2019)"},{"key":"2_CR43","unstructured":"Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through l_0 regularization. In: International Conference on Learning Representations (2018)"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Wu, J., Lin, W.: Thinet: a filter level pruning method for deep neural network compression. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 5058\u20135066 (2017)","DOI":"10.1109\/ICCV.2017.541"},{"key":"2_CR45","unstructured":"Ma, H., Chen, T., Hu, T.K., You, C., Xie, X., Wang, Z.: Good students play big lottery better. arXiv preprint arXiv:2101.03255 (2021)"},{"key":"2_CR46","unstructured":"Neill, J.O., Dutta, S., Assem, H.: Deep neural compression via concurrent pruning and self-distillation. arXiv preprint arXiv:2109.15014 (2021)"},{"key":"2_CR47","unstructured":"Sui, Y., Yin, M., Xie, Y., Phan, H., Aliari Zonouz, S., Yuan, B.: Chip: channel independence-based pruning for compact neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 24604\u201324616 (2021)"},{"key":"2_CR48","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.neunet.2022.01.012","volume":"148","author":"T Sun","year":"2022","unstructured":"Sun, T., Ding, S., Guo, L.: Low-degree term first in resnet, its variants and the whole neural network family. Neural Netw. 148, 155\u2013165 (2022)","journal-title":"Neural Netw."},{"key":"2_CR49","unstructured":"Tang, S., et al.: Boosting residual networks with group knowledge. arXiv preprint arXiv:2308.13772 (2023)"},{"key":"2_CR50","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"2_CR51","unstructured":"Veit, A., Wilber, M.J., Belongie, S.: Residual networks behave like ensembles of relatively shallow networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"2_CR52","unstructured":"Wang, H., Qin, C., Zhang, Y., Fu, Y.: Neural pruning via growing regularization. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"2_CR53","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, Q., Wang, Y., Yu, L., Hu, H.: Structured pruning for efficient convnets via incremental regularization. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852463"},{"key":"2_CR54","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Wu, Y.C., Liu, C.T., Chen, B.Y., Chien, S.Y.: Constraint-aware importance estimation for global filter pruning under multiple resource constraints. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 686\u2013687 (2020)","DOI":"10.1109\/CVPRW50498.2020.00351"},{"key":"2_CR56","doi-asserted-by":"crossref","unstructured":"Xia, B., et al.: Structured sparsity learning for efficient video super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22638\u201322647 (2023)","DOI":"10.1109\/CVPR52729.2023.02168"},{"key":"2_CR57","doi-asserted-by":"crossref","unstructured":"Xia, M., Zhong, Z., Chen, D.: Structured pruning learns compact and accurate models. arXiv preprint arXiv:2204.00408 (2022)","DOI":"10.18653\/v1\/2022.acl-long.107"},{"key":"2_CR58","unstructured":"Yang, H., Wen, W., Li, H.: DeepHoyer: learning sparser neural network with differentiable scale-invariant sparsity measures. In: International Conference on Learning Representations (2019)"},{"issue":"8","key":"2_CR59","doi-asserted-by":"publisher","first-page":"10267","DOI":"10.1109\/TPAMI.2023.3260903","volume":"45","author":"H Ye","year":"2023","unstructured":"Ye, H., Zhang, B., Chen, T., Fan, J., Wang, B.: Performance-aware approximation of global channel pruning for multitask CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 10267\u201310284 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR60","unstructured":"Ye, P., et al.: Stimulative training++: go beyond the performance limits of residual networks. arXiv preprint arXiv:2305.02507 (2023)"},{"key":"2_CR61","doi-asserted-by":"crossref","unstructured":"Ye, P., Li, B., Li, Y., Chen, T., Fan, J., Ouyang, W.: $$\\beta $$-darts: beta-decay regularization for differentiable architecture search. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10864\u201310873. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.01060"},{"key":"2_CR62","unstructured":"Ye, P., Tang, S., Li, B., Chen, T., Ouyang, W.: Stimulative training of residual networks: a social psychology perspective of loafing. In: Advances in Neural Information Processing Systems, vol. 35, pp. 3596\u20133608 (2022)"},{"key":"2_CR63","unstructured":"You, Z., Yan, K., Ye, J., Ma, M., Wang, P.: Gate decorator: global filter pruning method for accelerating deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"2_CR64","unstructured":"Yu, J., Huang, T.: AutoSlim: towards one-shot architecture search for channel numbers. arXiv preprint arXiv:1903.11728 (2019)"},{"key":"2_CR65","unstructured":"Yu, J., Yang, L., Xu, N., Yang, J., Huang, T.: Slimmable neural networks. arXiv preprint arXiv:1812.08928 (2018)"},{"key":"2_CR66","unstructured":"Yu, S., et al.: Unified visual transformer compression. arXiv preprint arXiv:2203.08243 (2022)"},{"key":"2_CR67","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: British Machine Vision Conference 2016. British Machine Vision Association (2016)","DOI":"10.5244\/C.30.87"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72983-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T09:53:42Z","timestamp":1730109222000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72983-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"ISBN":["9783031729829","9783031729836"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72983-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,29]]},"assertion":[{"value":"29 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}