{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:29:53Z","timestamp":1742963393869,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030670696"},{"type":"electronic","value":"9783030670702"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-67070-2_7","type":"book-chapter","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T20:02:51Z","timestamp":1611950571000},"page":"119-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Hybrid Composition Based Super-Resolution Network via Fine-Grained Channel Pruning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-781X","authenticated-orcid":false,"given":"Siang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2295-5433","authenticated-orcid":false,"given":"Kai","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7525-9672","authenticated-orcid":false,"given":"Bowen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4882-7504","authenticated-orcid":false,"given":"Dongliang","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8215-166X","authenticated-orcid":false,"given":"Haitian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0405-6290","authenticated-orcid":false,"given":"Luc","family":"Claesen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"7_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/978-3-030-01249-6_16","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ahn","year":"2018","unstructured":"Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 256\u2013272. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_16"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Ding, X., Ding, G., Guo, Y., Han, J.: Centripetal SGD for pruning very deep convolutional networks with complicated structure. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 4943\u20134953 (2019)","DOI":"10.1109\/CVPR.2019.00508"},{"key":"7_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184\u2013199. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13"},{"key":"7_CR5","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) European Conference on Computer Vision, ECCV"},{"issue":"2","key":"7_CR6","doi-asserted-by":"publisher","first-page":"12:1","DOI":"10.1145\/1944846.1944852","volume":"30","author":"G Freedman","year":"2011","unstructured":"Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 30(2), 12:1\u201312:11 (2011)","journal-title":"ACM Trans. Graph."},{"issue":"2","key":"7_CR7","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"WT Freeman","year":"2002","unstructured":"Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graphics Appl. 22(2), 56\u201365 (2002)","journal-title":"IEEE Comput. Graphics Appl."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Gao, S., Liu, X., Chien, L., Zhang, W., Alvarez, J.M.: VACL: variance-aware cross-layer regularization for pruning deep residual networks. In: International Conference on Computer Vision Workshops, ICCV Workshops, pp. 2980\u20132988 (2019)","DOI":"10.1109\/ICCVW.2019.00360"},{"key":"7_CR9","unstructured":"Guo, Y., Yao, A., Chen, Y.: Dynamic network surgery for efficient DNNs. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Annual Conference on Neural Information Processing, NeurIPS, pp. 1379\u20131387 (2016)"},{"key":"7_CR10","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. CoRR abs\/1506.02626 (2015)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1664\u20131673 (2018)","DOI":"10.1109\/CVPR.2018.00179"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: Lang, J. (ed.) International Joint Conference on Artificial Intelligence, IJCAI, pp. 2234\u20132240 (2018)","DOI":"10.24963\/ijcai.2018\/309"},{"key":"7_CR14","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: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 4340\u20134349 (2019)","DOI":"10.1109\/CVPR.2019.00447"},{"key":"7_CR15","unstructured":"Hu, H., Peng, R., Tai, Y., Tang, C.: Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. CoRR abs\/1607.03250 (2016)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Huang, Q., Zhou, S.K., You, S., Neumann, U.: Learning to prune filters in convolutional neural networks. In: Winter Conference on Applications of Computer Vision, WACV, pp. 709\u2013718 (2018)","DOI":"10.1109\/WACV.2018.00083"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Amsaleg, L., Huet, B., Larson, M.A., Gravier, G., Hung, H., Ngo, C., Ooi, W.T. (eds.) International Conference on Multimedia, MM, pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"7_CR18","unstructured":"Ignatov, A., et al.: Pirm challenge on perceptual image enhancement on smartphones: report. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1637\u20131645 (2016)","DOI":"10.1109\/CVPR.2016.181"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Lai, W., Huang, J., Ahuja, N., Yang, M.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 5835\u20135843 (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Lemaire, C., Achkar, A., Jodoin, P.: Structured pruning of neural networks with budget-aware regularization. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 9108\u20139116 (2019)","DOI":"10.1109\/CVPR.2019.00932"},{"key":"7_CR23","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: International Conference on Learning Representations, ICLR (2017)"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, pp. 1132\u20131140 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Lin, S., Ji, R., Li, Y., Wu, Y., Huang, F., Zhang, B.: Accelerating convolutional networks via global & dynamic filter pruning. In: Lang, J. (ed.) International Joint Conference on Artificial Intelligence, IJCAI, pp. 2425\u20132432 (2018)","DOI":"10.24963\/ijcai.2018\/336"},{"key":"7_CR26","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: International Conference on Computer Vision, ICCV, pp. 2755\u20132763 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 11264\u201311272 (2019)","DOI":"10.1109\/CVPR.2019.01152"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2790\u20132798 (2017)","DOI":"10.1109\/CVPR.2017.298"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: International Conference on Computer Vision, ICCV. pp. 4549\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.486"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L., et al.: Ntire 2017 challenge on single image super-resolution: Methods and results. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017","DOI":"10.1109\/CVPRW.2017.150"},{"key":"7_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-319-16817-3_8","volume-title":"Computer Vision \u2013 ACCV 2014","author":"R Timofte","year":"2015","unstructured":"Timofte, R., De\u00a0Smet, V., Van\u00a0Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111\u2013126. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16817-3_8"},{"key":"7_CR34","unstructured":"Timofte, R., Gu, S., Wu, J., Van Gool, L.: Ntire 2018 challenge on single image super-resolution: Methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 852\u2013863 (2018)"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V.D., Gool, L.V.: Anchored neighborhood regression for fast example-based super-resolution. In: International Conference on Computer Vision, ICCV, pp. 1920\u20131927 (2013)","DOI":"10.1109\/ICCV.2013.241"},{"key":"7_CR36","unstructured":"Wang, C., Li, Z., Shi, J.: Lightweight image super-resolution with adaptive weighted learning network. CoRR abs\/1904.02358 (2019)"},{"key":"7_CR37","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Annual Conference on Neural Information Processing Systems, NeurIPS, pp. 2074\u20132082 (2016)"},{"key":"7_CR38","unstructured":"Ye, J., Lu, X., Lin, Z., Wang, J.Z.: Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers. In: International Conference on Learning Representations, ICLR (2018)"},{"key":"7_CR39","unstructured":"You, Z., Yan, K., Ye, J., Ma, M., Wang, P.: Gate decorator: global filter pruning method for accelerating deep convolutional neural networks. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Annual Conference on Neural Information Processing, NeurIPS, pp. 2130\u20132141 (2019)"},{"key":"7_CR40","unstructured":"Zhang, K., Danelljan, M., Li, Y., Timofte, R., et al.: AIM 2020 challenge on efficient super-resolution: methods and results. In: European Conference on Computer Vision Workshops (2020)"},{"key":"7_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"7_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67070-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T23:09:49Z","timestamp":1738105789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67070-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030670696","9783030670702"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67070-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 January 2021","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}