Results 21 to 30 of about 73,468 (309)
Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
IEEE Access, 2021 Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems.
Hoda Sadeghzadeh +2 more
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Informatics in Medicine Unlocked, 2021 Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training ...
Saman Motamed +2 more
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Understanding of Convolutional Neural Network (CNN): A Review
International Journal of Robotics and Control Systems, 2023 The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans.
Purwono Purwono +5 more
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CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2021 11 pages, 14 figures, to be presented at IEEE VIS 2020. For a demo video, see https://youtu.be/HnWIHWFbuUQ . For a live demo, visit https://poloclub.github.io/cnn-explainer/
Zijie J. Wang +7 more
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Convolutional Neural Network (CNN): A comprehensive overview
International Journal of Multidisciplinary Research and Growth Evaluation, 2022 Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN
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A-CNN: Annularly Convolutional Neural Networks on Point Clouds [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution.
Artem Komarichev +2 more
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Continuous speech recognition by convolutional neural networks
工程科学学报, 2015 Convolutional neural networks (CNNs), which show success in achieving translation invariance for many image processing tasks, were investigated for continuous speech recognition.
ZHANG Qing-qing +3 more
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Attention-Based Convolutional LSTM for Describing Video
IEEE Access, 2020 Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the
Zhongyu Liu +4 more
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CNN 101: Interactive Visual Learning for Convolutional Neural Networks [PDF]
Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020 The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models.
Zijie J. Wang +7 more
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Geometrical aspects of lattice gauge equivariant convolutional neural networks [PDF]
, 2023 Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-Abelian lattice gauge theories without violating gauge symmetry.
Aronsson, Jimmy; orcid: +2 more
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