{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:35:36Z","timestamp":1763202936636,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031723346"},{"type":"electronic","value":"9783031723353"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72335-3_24","type":"book-chapter","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T14:03:01Z","timestamp":1726495381000},"page":"351-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Study in\u00a0Dataset Pruning for\u00a0Image Super-Resolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0290-7904","authenticated-orcid":false,"given":"Brian B.","family":"Moser","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8604-6207","authenticated-orcid":false,"given":"Federico","family":"Raue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-8255","authenticated-orcid":false,"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"24_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-58517-4_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Agarwal","year":"2020","unstructured":"Agarwal, S., Arora, H., Anand, S., Arora, C.: Contextual diversity for active learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 137\u2013153. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_9"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.621","volume":"7","author":"SMA Bashir","year":"2021","unstructured":"Bashir, S.M.A., Wang, Y., Khan, M., Niu, Y.: A comprehensive review of deep learning-based single image super-resolution. PeerJ Comput. Sci. 7, e621 (2021)","journal-title":"PeerJ Comput. Sci."},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)","DOI":"10.5244\/C.26.135"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.Y.: Dataset distillation by matching training trajectories. In: CVPR, pp. 4750\u20134759 (2022)","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.Y.: Generalizing dataset distillation via deep generative prior. In: CVPR, pp. 3739\u20133748 (2023)","DOI":"10.1109\/CVPR52729.2023.00364"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: CVPR, pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"24_CR8","unstructured":"Coleman, C., et al.: Selection via proxy: Efficient data selection for deep learning. arXiv preprint arXiv:1906.11829 (2019)"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: CVPR, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Ding, Q., Liang, Z., Wang, L., Wang, Y., Yang, J.: Not all patches are equal: hierarchical dataset condensation for single image super-resolution. IEEE Signal Process. Lett. (2023)","DOI":"10.1109\/LSP.2023.3329754"},{"issue":"2","key":"24_CR11","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE TPAMI 38(2), 295\u2013307 (2015)","journal-title":"IEEE TPAMI"},{"key":"24_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Dong","year":"2016","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.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Ganguli, D., et\u00a0al.: Predictability and surprise in large generative models. In: 2022 ACM Conference on Fairness, Accountability, and Transparency (2022)","DOI":"10.1145\/3531146.3533229"},{"key":"24_CR14","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NeurIPS Workshop (2015)"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: CVPR, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: CVPR, pp. 723\u2013731 (2018)","DOI":"10.1109\/CVPR.2018.00082"},{"key":"24_CR17","unstructured":"Katharopoulos, A., Fleuret, F.: Not all samples are created equal: Deep learning with importance sampling. In: ICML, pp. 2525\u20132534. PMLR (2018)"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: ICCV, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"24_CR19","unstructured":"Liu, Y., Liu, A., Gu, J., Zhang, Z., Wu, W., Qiao, Y., Dong, C.: Discovering distinctive\" semantics\" in super-resolution networks. arXiv preprint arXiv:2108.00406 (2021)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, vol.\u00a02, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"24_CR21","unstructured":"The Mathworks, Inc., Natick, Massachusetts: MATLAB version 9.3.0.713579 (R2017b) (2017)"},{"key":"24_CR22","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","volume":"76","author":"Y Matsui","year":"2017","unstructured":"Matsui, Y., et al.: Sketch-based manga retrieval using manga109 dataset. Multimedia Tools Appli. 76, 21811\u201321838 (2017)","journal-title":"Multimedia Tools Appli."},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: CVPR, pp. 3517\u20133526 (2021)","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Moser, B., Frolov, S., Raue, F., Palacio, S., Dengel, A.: Waving goodbye to low-res: A diffusion-wavelet approach for image super-resolution. arXiv preprint arXiv:2304.01994 (2023)","DOI":"10.1109\/IJCNN60899.2024.10651227"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Moser, B., Raue, F., Hees, J., Dengel, A.: Less is more: proxy datasets in nas approaches. In: CVPR, pp. 1953\u20131961 (2022)","DOI":"10.1109\/CVPRW56347.2022.00212"},{"key":"24_CR26","doi-asserted-by":"publisher","unstructured":"Moser, B.B., Frolov, S., Raue, F., Palacio, S., Dengel, A.: Dwa: differential wavelet amplifier for image super-resolution. pp. 232\u2013243. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-44210-0_19","DOI":"10.1007\/978-3-031-44210-0_19"},{"key":"24_CR27","unstructured":"Moser, B.B., Frolov, S., Raue, F., Palacio, S., Dengel, A.: Yoda: you only diffuse areas. an area-masked diffusion approach for image super-resolution. arXiv preprint arXiv:2308.07977 (2023)"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Moser, B.B., Raue, F., Frolov, S., Palacio, S., Hees, J., Dengel, A.: Hitchhiker\u2019s guide to super-resolution: introduction and recent advances. IEEE TPAMI (2023)","DOI":"10.1109\/TPAMI.2023.3243794"},{"key":"24_CR29","unstructured":"Moser, B.B., Raue, F., Palacio, S., Frolov, S., Dengel, A.: Latent dataset distillation with diffusion models. arXiv preprint arXiv:2403.03881 (2024)"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Moser, B.B., Shanbhag, A.S., Raue, F., Frolov, S., Palacio, S., Dengel, A.: Diffusion models, image super-resolution and everything: survey. arXiv preprint arXiv:2401.00736 (2024)","DOI":"10.1109\/TNNLS.2024.3476671"},{"key":"24_CR31","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. arXiv preprint arXiv:2011.00050 (2020)"},{"key":"24_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-030-58610-2_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Niu","year":"2020","unstructured":"Niu, B., et al.: Single image super-resolution via a holistic attention network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 191\u2013207. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_12"},{"key":"24_CR33","first-page":"20596","volume":"34","author":"M Paul","year":"2021","unstructured":"Paul, M., Ganguli, S., Dziugaite, G.K.: Deep learning on a data diet: finding important examples early in training. NeurIPS 34, 20596\u201320607 (2021)","journal-title":"NeurIPS"},{"key":"24_CR34","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489 (2017)"},{"key":"24_CR35","unstructured":"Shleifer, S., Prokop, E.: Using small proxy datasets to accelerate hyperparameter search. arXiv preprint arXiv:1906.04887 (2019)"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: CVPR, pp. 3147\u20133155 (2017)","DOI":"10.1109\/CVPR.2017.298"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Valsesia, D., Magli, E.: Permutation invariance and uncertainty in multitemporal image super-resolution. arXiv preprint arXiv:2105.12409 (2021)","DOI":"10.1109\/TGRS.2021.3130673"},{"key":"24_CR38","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)"},{"key":"24_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-642-27413-8_47","volume-title":"Curves and Surfaces","author":"R Zeyde","year":"2012","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711\u2013730. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47"},{"key":"24_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: ECCV, pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"24_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: CVPR, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"24_CR42","unstructured":"Zhao, B., Bilen, H.: Synthesizing informative training samples with gan. arXiv preprint arXiv:2204.07513 (2022)"},{"key":"24_CR43","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. arXiv preprint arXiv:2006.05929 (2020)"},{"key":"24_CR44","first-page":"3499","volume":"33","author":"S Zhou","year":"2020","unstructured":"Zhou, S., Zhang, J., Zuo, W., Loy, C.C.: Cross-scale internal graph neural network for image super-resolution. NeurIPS 33, 3499\u20133509 (2020)","journal-title":"NeurIPS"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72335-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T06:26:00Z","timestamp":1732775160000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72335-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723346","9783031723353"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72335-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lugano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","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":"17 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}