{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T23:05:35Z","timestamp":1779318335275,"version":"3.51.4"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032231758","type":"print"},{"value":"9783032231765","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-23176-5_10","type":"book-chapter","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T22:09:00Z","timestamp":1779314940000},"page":"127-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Curriculum Learning on\u00a0Image-Denoising Autoencoders"],"prefix":"10.1007","author":[{"given":"Fernando","family":"Pereira dos Santos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maur\u00edcio","family":"Schiezaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","unstructured":"dos Santos, F.P., Ponti, M.A.: Homogeneity index as stopping criterion for anisotropic diffusion filter. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns: 18th International Conference, CAIP: Salerno, Italy, September 3\u20135, 2019, Proceedings, Part II 18, pp. 269\u2013280. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29891-3_24","DOI":"10.1007\/978-3-030-29891-3_24"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Ribeiro, P.H.A., Souza, J.C.D.O.: Image denoising with non-convex models: a comparison between BDCA and nmBDCA. In: 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1\u20136. IEEE (2024)","DOI":"10.1109\/SIBGRAPI62404.2024.10716308"},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.nima.2017.12.050","volume":"884","author":"D Lee","year":"2018","unstructured":"Lee, D., Choi, S., Kim, H.-J.: Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography. Nucl. Instrum. Methods Phys. Res., Sect. A 884, 97\u2013104 (2018)","journal-title":"Nucl. Instrum. Methods Phys. Res., Sect. A"},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/TNNLS.2018.2838679","volume":"30","author":"A Majumdar","year":"2018","unstructured":"Majumdar, A.: Blind denoising autoencoder. IEEE Trans. Neural Networks Learn. Syst. 30(1), 312\u2013317 (2018)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Ponti, M.A., Ribeiro, L.S.F., Nazare, T.S., Bui, T., Collomosse, J.: Everything you wanted to know about deep learning for computer vision but were afraid to ask. In: 30th SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T), pp. 17\u201341. IEEE (2017)","DOI":"10.1109\/SIBGRAPI-T.2017.12"},{"key":"10_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110176","volume":"138","author":"P Li","year":"2023","unstructured":"Li, P., Pei, Y., Li, J.: A comprehensive survey on design and application of autoencoder in deep learning. Appl. Soft Comput. 138, 110176 (2023)","journal-title":"Appl. Soft Comput."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096\u20131103 (2008)","DOI":"10.1145\/1390156.1390294"},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s10462-023-10662-6","volume":"57","author":"K Berahmand","year":"2024","unstructured":"Berahmand, K., Daneshfar, F., Salehi, E.S., Li, Y., Xu, Y.: Autoencoders and their applications in machine learning: a survey. Artif. Intell. Rev. 57(2), 28 (2024)","journal-title":"Artif. Intell. Rev."},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Ponti, M.A., dos Santos, F.P., Ribeiro, L.S., Cavallari, G.B.: Training deep networks from zero to hero: avoiding pitfalls and going beyond. In: 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 9\u201316. IEEE (2021)","DOI":"10.1109\/SIBGRAPI54419.2021.00011"},{"issue":"05","key":"10_CR10","first-page":"7839","volume":"34","author":"J Guo","year":"2020","unstructured":"Guo, J., Tan, X., Xu, L., Qin, T., Chen, E., Liu, T.-Y.: Fine-tuning by curriculum learning for non-autoregressive neural machine translation. Proc. AAAI Conf. Artif. Intell. 34(05), 7839\u20137846 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Braun, S., Neil, D., Liu, S.-C.: A curriculum learning method for improved noise robustness in automatic speech recognition. In: 25th European Signal Processing Conference (EUSIPCO), vol. 2017, pp. 548\u2013552. IEEE (2017)","DOI":"10.23919\/EUSIPCO.2017.8081267"},{"key":"10_CR12","unstructured":"Kim, J.-Y., Go, H., Kwon, S., Kim, H.-G.: Denoising task difficulty-based curriculum for training diffusion models. arXiv preprint arXiv:2403.10348 (2024)"},{"issue":"7","key":"10_CR13","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/LSP.2018.2843295","volume":"25","author":"T Yu","year":"2018","unstructured":"Yu, T., Guo, C., Wang, L., Xiang, S., Pan, C.: Self-paced autoencoder. IEEE Signal Process. Lett. 25(7), 1054\u20131058 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yue, Y., Lu, R., Liu, T., Zhong, Z., Song, S., Huang, G.: EfficientTrain: exploring generalized curriculum learning for training visual backbones. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5852\u20135864 (2023)","DOI":"10.1109\/ICCV51070.2023.00538"},{"key":"10_CR15","unstructured":"Hacohen, G., Weinshall, D.: On the power of curriculum learning in training deep networks. In: International Conference on Machine Learning, pp. 2535\u20132544. PMLR (2019)"},{"key":"10_CR16","first-page":"1","volume":"71","author":"P Singh","year":"2022","unstructured":"Singh, P., Sharma, A.: Attention-based convolutional denoising autoencoder for two-lead ECG denoising and arrhythmia classification. IEEE Trans. Instrum. Meas. 71, 1\u201310 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Gao, L., Jin, X., Zhang, Y., Wang, S., Sun, Z.: MSCNet: multi-scale connected network for image denoising. In: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2024)","DOI":"10.1109\/IJCNN60899.2024.10650703"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41\u201348 (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"10_CR19","unstructured":"Weinshall, D., Cohen, G., Amir, D.: Curriculum learning by transfer learning: Theory and experiments with deep networks. In: International Conference on Machine Learning, pp. 5238\u20135246. PMLR (2018)"},{"issue":"9","key":"10_CR20","first-page":"4555","volume":"44","author":"X Wang","year":"2021","unstructured":"Wang, X., Chen, Y., Zhu, W.: A survey on curriculum learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4555\u20134576 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR21","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":"10_CR22","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neunet.2020.08.016","volume":"132","author":"FP Dos Santos","year":"2020","unstructured":"Dos Santos, F.P., Zor, C., Kittler, J., Ponti, M.A.: Learning image features with fewer labels using a semi-supervised deep convolutional network. Neural Netw. 132, 131\u2013143 (2020)","journal-title":"Neural Netw."},{"key":"10_CR23","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807\u2013814 (2010)"},{"issue":"1","key":"10_CR24","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MSP.2008.930649","volume":"26","author":"Z Wang","year":"2009","unstructured":"Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98\u2013117 (2009)","journal-title":"IEEE Signal Process. Mag."},{"key":"10_CR25","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. Rep, Citeseer (2009)"},{"issue":"1","key":"10_CR26","doi-asserted-by":"publisher","first-page":"26","DOI":"10.2478\/ausi-2018-0002","volume":"10","author":"H Mure\u015fan","year":"2018","unstructured":"Mure\u015fan, H., Oltean, M.: Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica 10(1), 26\u201342 (2018)","journal-title":"Acta Universitatis Sapientiae, Informatica"},{"issue":"11","key":"10_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"10_CR28","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"10_CR29","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: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV,: vol. 2, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"10_CR30","unstructured":"Kodak, E.: Kodak lossless true color image suite (photocd pcd0992), vol.\u00a06, p.\u00a02 (1993). http:\/\/r0k.us\/graphics\/kodak"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Sun, L., Hays, J.: Super-resolution from internet-scale scene matching. In: IEEE International Conference on Computational Photography (ICCP), pp. 1\u201312. IEEE (2012)","DOI":"10.1109\/ICCPhot.2012.6215221"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Huang, J.-B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"3","key":"10_CR33","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1109\/TIP.2012.2219544","volume":"22","author":"W Liu","year":"2012","unstructured":"Liu, W., Lin, W.: Additive white gaussian noise level estimation in SVD domain for images. IEEE Trans. Image Process. 22(3), 872\u2013883 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10_CR34","first-page":"121","volume":"9","author":"S Mohamed","year":"2019","unstructured":"Mohamed, S., Ejbali, R., Zaied, M.: Denoising autoencoder with dropout based network anomaly detection. ICSEA 9(1), 121\u2013130 (2019)","journal-title":"ICSEA"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Kowalik-Urbaniak, I.A., et al.: Modelling of subjective radiological assessments with objective image quality measures of brain and body CT images. In: International Conference Image Analysis and Recognition, pp. 3\u201313. Springer (2015)","DOI":"10.1007\/978-3-319-20801-5_1"},{"issue":"4","key":"10_CR36","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104878","volume":"156","author":"OV Ribeiro-Filho","year":"2025","unstructured":"Ribeiro-Filho, O.V., Ponti, M.A., Curilem, M., Rios, R.A.: Integrating wavelet transformation for end-to-end direct signal classification. Digital Signal Processing 156, 104878 (2025)","journal-title":"Digital Signal Processing"},{"key":"10_CR38","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"10_CR39","unstructured":"Masters, D., Luschi, C.: Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612 (2018)"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23176-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T22:09:06Z","timestamp":1779314946000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23176-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032231758","9783032231765"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23176-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bogot\u00e1","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Colombia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ingenieria.unal.edu.co\/ciarp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}