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Artículos de revistas sobre el tema "Super learning"

Autor: Grafiati

Publicado: 6 de julio de 2024

Última modificación: 31 de julio de 2025

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1

Long, Jun, Jinhuan Zhang, and Ping Du. "Super-sampling by learning-based super-resolution." International Journal of Computational Science and Engineering 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijcse.2019.10020177.

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2

Du, Ping, Jinhuan Zhang, and Jun Long. "Super-sampling by learning-based super-resolution." International Journal of Computational Science and Engineering 21, no. 2 (2020): 249. http://dx.doi.org/10.1504/ijcse.2020.105731.

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3

Haris, Muhammad, M. Rahmat Widyanto, and Hajime Nobuhara. "Inception learning super-resolution." Applied Optics 56, no. 22 (2017): 6043. http://dx.doi.org/10.1364/ao.56.006043.

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4

GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.

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This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate mol

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5

Aitken, Michael R. F., Mark J. W. Larkin, and Anthony Dickinson. "Super-learning of Causal Judgements." Quarterly Journal of Experimental Psychology B 53, no. 1 (2000): 59–81. http://dx.doi.org/10.1080/027249900392995.

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6

Lim, Alane. "Machine learning method puts the “super” in super-resolution spectroscopy." Scilight 2021, no. 49 (2021): 491108. http://dx.doi.org/10.1063/10.0009031.

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7

Han, Tong, Li Zhao, and Chuang Wang. "Research on Super-resolution Image Based on Deep Learning." International Journal of Advanced Network, Monitoring and Controls 8, no. 1 (2023): 58–65. http://dx.doi.org/10.2478/ijanmc-2023-0046.

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Abstract Image super-resolution is a kind of important image processing technology in computer vision and image processing. It refers to the process of recovering high-resolution image from low-resolution image. It has a wide range of real-world applications, such as medical imaging, security and others. In addition to improving image perception quality, it also helps improve other computer vision tasks. Compared with traditional methods, deep learning methods show better reconstruction results in the field of image super-resolution reconstruction, and have gradually developed into the mainstr

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8

Jiang, Jingyu, Li Zhao, and Yan Jiao. "Research on Image Super-resolution Reconstruction Based on Deep Learning." International Journal of Advanced Network, Monitoring and Controls 7, no. 1 (2022): 1–21. http://dx.doi.org/10.2478/ijanmc-2022-0001.

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Abstract Image super-resolution reconstruction (SR) aims to use a specific algorithm to restore a low-resolution blurred image in the same scene into a high-resolution clear image. Due to its wide application value and theoretical value, image super-resolution reconstruction technology has become a research hotspot in the field of computer vision and image processing, and has attracted widespread attention from researchers. Compared with traditional methods, deep learning methods have shown better reconstruction effects in the field of image super-resolution reconstruction, and have gradually

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9

Singh, Kajol, and Manish Saxena. "A Review on Medical Image Super Resolution with Application of Deep Learning." SMART MOVES JOURNAL IJOSCIENCE 7, no. 2 (2021): 25–29. http://dx.doi.org/10.24113/ijoscience.v7i2.368.

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Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, w

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10

R. Mhatre, Sneha, and Jagdish W. Bakal. "A Review of Image Super Resolution using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 145–49. http://dx.doi.org/10.17762/ijritcc.v11i5s.6638.

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The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designe

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11

Demontis, Ambra, Marco Melis, Battista Biggio, Giorgio Fumera, and Fabio Roli. "Super-Sparse Learning in Similarity Spaces." IEEE Computational Intelligence Magazine 11, no. 4 (2016): 36–45. http://dx.doi.org/10.1109/mci.2016.2601702.

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12

Strack, Rita. "Deep learning advances super-resolution imaging." Nature Methods 15, no. 6 (2018): 403. http://dx.doi.org/10.1038/s41592-018-0028-9.

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13

Kita, Koji, Michifumi Yoshioka, Katsufumi Inoue, Naru Inage, and Shohei Tsunekawa. "Figure Patches Learning-based Super-Resolution." IEEJ Transactions on Electronics, Information and Systems 136, no. 7 (2016): 929–37. http://dx.doi.org/10.1541/ieejeiss.136.929.

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14

Yang, Wenming, Fei Zhou, Rui Zhu, Kazuhiro Fukui, Guijin Wang, and Jing-Hao Xue. "Deep learning for image super-resolution." Neurocomputing 398 (July 2020): 291–92. http://dx.doi.org/10.1016/j.neucom.2019.09.091.

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15

Wang, Wenjun, Chao Ren, Xiaohai He, Honggang Chen, and Linbo Qing. "Video Super-Resolution via Residual Learning." IEEE Access 6 (2018): 23767–77. http://dx.doi.org/10.1109/access.2018.2829908.

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16

Yi Tang and Yuan Yuan. "Learning From Errors in Super-Resolution." IEEE Transactions on Cybernetics 44, no. 11 (2014): 2143–54. http://dx.doi.org/10.1109/tcyb.2014.2301732.

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17

Liu, Huanyu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan, and Xiaqiong Yu. "DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution." Journal of Healthcare Engineering 2021 (April 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/5594649.

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Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-

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18

He, H., K. Gao, W. Tan, et al. "IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 31–37. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-31-2022.

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Abstract. Automated building footprints extraction from High Spatial Resolution (HSR) remote sensing images plays important roles in urban planning and management, and hazard and disease control. However, HSR images are not always available in practice. In these cases, super-resolution, especially deep learning (DL)-based methods, can provide higher spatial resolution images given lower resolution images. In a variety of remote sensing applications, DL based super-resolution methods are widely used. However, there are few studies focusing on the impact of DL-based super-resolution on building

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19

Ordyniak, S., and S. Szeider. "Parameterized Complexity Results for Exact Bayesian Network Structure Learning." Journal of Artificial Intelligence Research 46 (March 5, 2013): 263–302. http://dx.doi.org/10.1613/jair.3744.

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Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian network structure learning under graph theoretic restrictions on the (directed) super-structure. The super-structure is an undirected graph that contains as subgraphs the skeletons of solution networks. We introduce the directed super-structure as a natural generalization of its undirected counterpart. Our results apply to several variants of score-based

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20

Pllana, Duli. "Combining Teaching Strategies, Learning Strategies, and Elements of Super Learning Principles." Advances in Social Sciences Research Journal 8, no. 6 (2021): 288–301. http://dx.doi.org/10.14738/assrj.86.10366.

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Blending teaching strategies, learning strategies, and elements of super learning principles increase learning outcomes tremendously in any case, situation, or academic subject. Employing teaching and learning strategies adequately impact on an interactive session (academic subjects or any field) to a great degree, enhance learners’ motivation significantly, improve self confidence and self esteem of learners considerably, and soar learning outcomes substiantly. It is impossible to combine all learning and teaching strategies (there are many techniques, and a small space time to incorporate th

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21

Hatta, Heliza Rahmania, Sri Nurdiati, Irman Hermadi, and Maman Turjaman. "Grade Classification of Agarwood Sapwood Using Deep Learning." JOIV : International Journal on Informatics Visualization 8, no. 4 (2024): 2075. https://doi.org/10.62527/joiv.8.4.2257.

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The agarwood tree (Aquilaria sp.) is a tree that produces agarwood, which is a black resin that has a distinctive fragrant smell. In Indonesia, one that is commonly traded is sapwood agarwood. Agarwood sapwood is black or brownish-black wood obtained from the parts of the agarwood-producing tree containing a strong aromatic mastic. Based on the Indonesian National Standard (SNI) 7631:2018, agarwood sapwood has three classes: Super Double, Super A, and Super B. However, many agarwood farmers need to learn to differentiate and classify the agarwood sapwood classes, and traders exploit this to bu

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22

Jian, Zhang, Xu Tengteng, Qian Jianjun, et al. "Single Image Self-Learning Super-Resolution with Robust Matrix Regression." AATCC Journal of Research 8, no. 1_suppl (2021): 135–42. http://dx.doi.org/10.14504/ajr.8.s1.17.

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The similarity measure plays the key role in the self-learning framework for single image super-resolution. This paper involves matrix regression with properties of robustness and two-dimensional structure to measure the similarity between image blocks and enhance the effect of super-resolution. Specifically, we use the minimal nuclear norm of representation error as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the similarity between high- and low-resolution image blocks. Evaluation on several images with different interference and experimental results o

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23

Lin, Xu, Qingqing Zhang, Hongyue Wang, et al. "A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning." Remote Sensing 14, no. 9 (2022): 2181. http://dx.doi.org/10.3390/rs14092181.

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The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on t

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24

He, Yifan, Wei Cao, Xiaofeng Du, and Changlin Chen. "Internal Learning for Image Super-Resolution by Adaptive Feature Transform." Symmetry 12, no. 10 (2020): 1686. http://dx.doi.org/10.3390/sym12101686.

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Recent years have witnessed the great success of image super-resolution based on deep learning. However, it is hard to adapt a well-trained deep model for a specific image for further improvement. Since the internal repetition of patterns is widely observed in visual entities, internal self-similarity is expected to help improve image super-resolution. In this paper, we focus on exploiting a complementary relation between external and internal example-based super-resolution methods. Specifically, we first develop a basic network learning external prior from large scale training data and then l

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25

Davies, Molly Margaret, and Mark J. van der Laan. "Optimal Spatial Prediction Using Ensemble Machine Learning." International Journal of Biostatistics 12, no. 1 (2016): 179–201. http://dx.doi.org/10.1515/ijb-2014-0060.

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Abstract Spatial prediction is an important problem in many scientific disciplines. Super Learner is an ensemble prediction approach related to stacked generalization that uses cross-validation to search for the optimal predictor amongst all convex combinations of a heterogeneous candidate set. It has been applied to non-spatial data, where theoretical results demonstrate it will perform asymptotically at least as well as the best candidate under consideration. We review these optimality properties and discuss the assumptions required in order for them to hold for spatial prediction problems.

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26

Maftuh, Muhammad Kholidin, and Dayat Hidayat. "THE EFFECT OF SUPERITEM LEARNING MODEL ON INCREASING STUDENTs LEARNING ACHIEVEMENTS." (JIML) JOURNAL OF INNOVATIVE MATHEMATICS LEARNING 1, no. 4 (2018): 367. http://dx.doi.org/10.22460/jiml.v1i4.p367-373.

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This study aims to see the influence of super-learning models on student achievement. This research is rarely done because of the lack of literature. In learning mathematics, it should not be direct to complex or complex concepts, but it must start from a simple concept. The aim to be achieved in this study is to determine whether or not the influence of super-learning learning models on quadrilateral learning on improving student learning achievement. The population in this study were Pangkalan 2 junior high school students. Samples were taken using makeshift sample techniques where the entir

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27

Li, Xiaoyan, Lefei Zhang, and Jane You. "Domain Transfer Learning for Hyperspectral Image Super-Resolution." Remote Sensing 11, no. 6 (2019): 694. http://dx.doi.org/10.3390/rs11060694.

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A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-reso

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28

Rosnani. "Pengaruh Model Pembelajaran Super Brain Terhadap Hasil Belajar Matematika Siswa Kota Parepare." Tautologi: Journal of Mathematics Education 1, no. 1 (2023): 30–34. http://dx.doi.org/10.31850/tautologi.v1i1.1908.

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The background of this research is that, in the process of learning mathematics, students are less able to develop learning materials, this is evidenced by the average daily test scores of students who are still below the KKM value (75), one way to overcome this problem is to apply the super brain learning model. This study aims to determine that the application of the super brain learning model has a positive effect on mathematics learning outcomes for class VIII UPTD SMP Negeri 1 Parepare students. This type of research is an experimental research type. The variables studied in this study we

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29

Nisa’, Chintya Hilyatun, Kholil Puspitasari, and Masnida Masnida. "Inisiatif Super Student: Meningkatkan Motivasi Belajar Siswa MA melalui Seminar." Santri : Journal of Student Engagement 4, no. 1 (2025): 54–67. https://doi.org/10.55352/santri.v4i1.1274.

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This study aims to analyze the extent to which the specially designed Super Student seminar can increase student learning motivation compared to conventional learning methods. The object of service regarding increasing learning motivation through the Super Student Initiative seminar is located at Madrasah Aliyah (MA) Darul Magfur Blumbangan Banyuwangi. The design in this service uses a service method with the type of CBPR (Community Based Participatory Research). The stages in this service include Laying the foundation, Research Planning, Information Gathering and Analysis and Acting on findin

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30

Shashi, Kiran Seetharamaswamy, and Kaggere Veeranna Suresh. "Super resolution image reconstruction via dual dictionary learning in sparse environment." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4970–77. https://doi.org/10.11591/ijece.v12i5.pp4970-4977.

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Patch-based super resolution is a method in which spatial features from a low-resolution (LR) patch are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted. These coefficients obtained are used to recover HR patch. One dictionary is trained for LR image patches, and another dictionary is trained for HR image patches and both dictionaries are jointly trained. In the proposed method, high frequency (HF) details required are treated as combination of main high frequency (MHF) and residual high frequency (RHF). Hence, d

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31

Geiss, Andrew, Sam J. Silva, and Joseph C. Hardin. "Downscaling atmospheric chemistry simulations with physically consistent deep learning." Geoscientific Model Development 15, no. 17 (2022): 6677–94. http://dx.doi.org/10.5194/gmd-15-6677-2022.

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Abstract. Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super-resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super-resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpo

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32

Wu, Haozhe. "Super-Resolution of Lightweight Images Based on Deep Learning." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 456–60. http://dx.doi.org/10.54097/f8y87181.

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As digital imaging technology advances, a significant breakthrough is the emergence of super-resolution technology, a method to enhance the quality of low-resolution images to high-resolution. When there are some developing of the newest digital camera's skills, the super-resolution technology is appearing and getting more and more importance. In the simplest terms, the image super-resolution skill is the technology of which one cover the process from low resolution images to high resolution images skills. This technology differs from the traditional approach in that it is attracting more and

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33

Leli, Vito M., Saeed Osat, Timur Tlyachev, Dmitry V. Dylov, and Jacob D. Biamonte. "Deep learning super-diffusion in multiplex networks." Journal of Physics: Complexity 2, no. 3 (2021): 035011. http://dx.doi.org/10.1088/2632-072x/abe6e9.

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34

Heo, Bo-Young, and Byung Cheol Song. "Learning-based Super-resolution for Text Images." Journal of the Institute of Electronics and Information Engineers 52, no. 4 (2015): 175–83. http://dx.doi.org/10.5573/ieie.2015.52.4.175.

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35

Singh, Nisha, and Myna A.N. "Image Super-Resolution Using Deep Learning Technique." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 150–55. http://dx.doi.org/10.26438/ijcse/v6i7.150155.

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36

Chae, Byungjoo, Jinsun Park, Tae-Hyun Kim, and Donghyeon Cho. "Online Learning for Reference-Based Super-Resolution." Electronics 11, no. 7 (2022): 1064. http://dx.doi.org/10.3390/electronics11071064.

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Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images

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37

Qin, Yu, Yuxing Li, Zhizheng Zhuo, Zhiwen Liu, Yaou Liu, and Chuyang Ye. "Multimodal super-resolved q-space deep learning." Medical Image Analysis 71 (July 2021): 102085. http://dx.doi.org/10.1016/j.media.2021.102085.

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38

Chen, Chaofeng, Dihong Gong, Hao Wang, Zhifeng Li, and Kwan-Yee K. Wong. "Learning Spatial Attention for Face Super-Resolution." IEEE Transactions on Image Processing 30 (2021): 1219–31. http://dx.doi.org/10.1109/tip.2020.3043093.

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39

Kawulok, Michal, Pawel Benecki, Szymon Piechaczek, Krzysztof Hrynczenko, Daniel Kostrzewa, and Jakub Nalepa. "Deep Learning for Multiple-Image Super-Resolution." IEEE Geoscience and Remote Sensing Letters 17, no. 6 (2020): 1062–66. http://dx.doi.org/10.1109/lgrs.2019.2940483.

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40

Jiang, Zhuqing, Honghui Zhu, Yue Lu, Guodong Ju, and Aidong Men. "Lightweight Super-Resolution Using Deep Neural Learning." IEEE Transactions on Broadcasting 66, no. 4 (2020): 814–23. http://dx.doi.org/10.1109/tbc.2020.2977513.

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41

Kumar, Neeraj, and Amit Sethi. "Fast Learning-Based Single Image Super-Resolution." IEEE Transactions on Multimedia 18, no. 8 (2016): 1504–15. http://dx.doi.org/10.1109/tmm.2016.2571625.

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42

Huang, Weiqin, Xiaorui Li, Yikai Gu, Xiaofu Du, and Xiancheng Zhu. "Learning Enriched Features for Image Super Resolution." IEEE Access 10 (2022): 113583–97. http://dx.doi.org/10.1109/access.2022.3216672.

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43

Tang, Yi, Pingkun Yan, Yuan Yuan, and Xuelong Li. "Single-image super-resolution via local learning." International Journal of Machine Learning and Cybernetics 2, no. 1 (2011): 15–23. http://dx.doi.org/10.1007/s13042-011-0011-6.

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44

Shamsolmoali, Pourya, Abdul Hamid Sadka, Huiyu Zhou, and Wankou Yang. "Advanced deep learning for image super-resolution." Signal Processing: Image Communication 82 (March 2020): 115732. http://dx.doi.org/10.1016/j.image.2019.115732.

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45

Naimi, Ashley I., and Laura B. Balzer. "Stacked generalization: an introduction to super learning." European Journal of Epidemiology 33, no. 5 (2018): 459–64. http://dx.doi.org/10.1007/s10654-018-0390-z.

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46

Chaudhari, Akshay S., Zhongnan Fang, Feliks Kogan, et al. "Super‐resolution musculoskeletal MRI using deep learning." Magnetic Resonance in Medicine 80, no. 5 (2018): 2139–54. http://dx.doi.org/10.1002/mrm.27178.

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47

Hasan, Zahraa. "Deep Learning for Super Resolution and Applications." Galoitica: Journal of Mathematical Structures and Applications 8, no. 2 (2023): 34–42. http://dx.doi.org/10.54216/gjmsa.080204.

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High-resolution technologies are aimed at obtaining a high-resolution image from a low-resolution image, and the importance of this field has increased due to the emergence of the need to have high-resolution images in many important applications such as medical, security, and other images. Methods for obtaining ultra-high-resolution images have developed after the advent of Deep Learning Technologies, which have shown good results in this task, Due to the importance of the field of ultra-high-resolution images and deep learning, In this article we will explain one of the deep learning models

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48

Yang, Guangtong, Chen Li, Yudong Yao, Ge Wang, and Yueyang Teng. "Quasi-supervised learning for super-resolution PET." Computerized Medical Imaging and Graphics 113 (April 2024): 102351. http://dx.doi.org/10.1016/j.compmedimag.2024.102351.

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49

Wu, Chao, and Yuan Jing. "Unsupervised super resolution using dual contrastive learning." Neurocomputing 630 (May 2025): 129649. https://doi.org/10.1016/j.neucom.2025.129649.

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50

Zhao, Hui-Jia, Jie Lu, Wen-Xiu Guo, and Xiao-Ping Lu. "Neural Operator for Planetary Remote Sensing Super-Resolution with Spectral Learning." Mathematics 12, no. 22 (2024): 3461. http://dx.doi.org/10.3390/math12223461.

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High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environments. Conventional super-resolution methods typically employ separate models for different scales, treating them as independent tasks. This approach limits deployment and real-time applications in planetary remote sensing. Moreover, capturing global context is crucial in planetary remote sensing images due to their contextual similarities. To addr

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