Show simple item record

dc.contributor.authorVarela, Noel
dc.contributor.authorComas-González, Zoe
dc.contributor.authorTernera-Muñoz, Yesith R
dc.contributor.authorEsmeral-Romero, Ernesto F
dc.contributor.authorLizardo Zelaya, Nelson Alberto
dc.description.abstractSince 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceProcedia Computer Sciencespa
dc.subjectDynamic trainingspa
dc.subjectDeep convolutional networksspa
dc.subjectImage classificationspa
dc.titleMethod for classifying images in databases through deep convolutional networksspa
dc.typeArtículo de revistaspa
dcterms.references[1] Singh, R., Khurana, R., Kushwaha, A. K. S., & Srivastava, R. (2020). Combining CNN streams of dynamic image and depth data for action recognition. Multimedia Systems,
dcterms.references[2] Mostafa, H., & Wang, X. (2019). Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. arXiv preprint
dcterms.references[3] Viloria, A., Angulo, M. G., Kamatkar, S. J., de la Hoz – Hernandez, J., Guiliany, J. G., Bilbao, O. R., & Hernandez-P, H. (2020). Prediction Rules in E-Learning Systems Using Genetic Programming. In Smart Innovation, Systems and Technologies (Vol. 164, pp. 55–63). Springer.
dcterms.references[4] Zheng, Q., Yang, M., Tian, X., Jiang, N., & Wang, D. (2020). A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification. Discrete Dynamics in Nature and Society,
dcterms.references[5] Haque, N., Reddy, N. D., & Krishna, K. M. (2017). Joint semantic and motion segmentation for dynamic scenes using deep convolutional networks. arXiv preprint
dcterms.references[6] Wang, H. M. X. (2019). Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization. arXiv preprint
dcterms.references[7] Tang, M., Liu, Y., & Durlofsky, L. J. (2020). A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems. Journal of Computational Physics,
dcterms.references[8] Yang, J., Liang, J., Shen, H., Wang, K., Rosin, P. L., & Yang, M. H. (2018). Dynamic match kernel with deep convolutional features for image retrieval. IEEE Transactions on Image Processing, 27(11),
dcterms.references[9] Popov, V., Shakev, N., Ahmed, S., & Toplaov, A. (2018, September). Recognition of Dynamic Targets using a Deep Convolutional Neural Network. In ANNA'18; Advances in Neural Networks and Applications 2018 (pp. 1-6).
dcterms.references[10] Aimone, J. B., & Severa, W. M. (2017). Context-modulation of hippocampal dynamics and deep convolutional networks. arXiv preprint
dcterms.references[11] Nah, S., Hyun Kim, T., & Mu Lee, K. (2017). Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3883-3891).spa
dcterms.references[12] Manessi, F., Rozza, A., & Manzo, M. (2020). Dynamic graph convolutional networks. Pattern Recognition, 97,
dcterms.references[13] Mo, S., Zhu, Y., Zabaras, N., Shi, X., & Wu, J. (2019). Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. Water Resources Research, 55(1),
dcterms.references[14] Viloria, A., Varela, N., Lezama, O. B. P., Llinás, N. O., Flores, Y., Palma, H. H., … Marín-González, F. (2020). Classification of Digitized Documents Applying Neural Networks. In Lecture Notes in Electrical Engineering (Vol. 637, pp. 213–220). Springer.
dcterms.references[15] Shao, R., Lan, X., & Yuen, P. C. (2017, October). Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing. In 2017 IEEE International Joint Conference on Biometrics (IJCB) (pp. 748-755).
dcterms.references[16] Bak, C., Erdem, A., & Erdem, E. (2016). Two-stream convolutional networks for dynamic saliency prediction. arXiv preprint arXiv:1607.04730, 2(3),

Files in this item


This item appears in the following Collection(s)

  • Artículos científicos [2641]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal