Results 41 to 50 of about 2,004,297 (387)
Content-aware convolutional neural networks [PDF]
Neural Networks, 2021 Accepted by Neural ...
Mingkui Tan +7 more
openaire +4 more sources
Relation-Shape Convolutional Neural Network for Point Cloud Analysis [PDF]
Computer Vision and Pattern Recognition, 2019 Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration ...
Yongcheng Liu +3 more
semanticscholar +1 more source
Image Denoising with Graph-Convolutional Neural Networks [PDF]
, 2019 Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks can outperform classical model-based techniques, because they can capture ...
Fracastoro, Giulia +2 more
core +2 more sources
Convolutional Neural Networks With Dynamic Regularization [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training.
Yi Wang +3 more
openaire +5 more sources
Detection of exomoons in simulated light curves with a regularized convolutional neural network
, 2020 Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets.
Alshehhi, Rasha +3 more
core +1 more source
Hyper-Convolution Networks for Biomedical Image Segmentation [PDF]
, 2021 The convolution operation is a central building block of neural network architectures widely used in computer vision. The size of the convolution kernels determines both the expressiveness of convolutional neural networks (CNN), as well as the number of learnable parameters.
arxiv +1 more source
Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Yang, M. (author) +2 more
openaire +3 more sources
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection [PDF]
European Conference on Computer Vision, 2016 A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network.
Zhaowei Cai +3 more
semanticscholar +1 more source
Diagnostics, 2022 Background: Otitis media includes several common inflammatory conditions of the middle ear that can have severe complications if left untreated. Correctly identifying otitis media can be difficult and a screening system supported by machine learning ...
Josefin Sandström +4 more
doaj +1 more source
VC dimensions of group convolutional neural networks [PDF]
arXiv, 2022 We study the generalization capacity of group convolutional neural networks. We identify precise estimates for the VC dimensions of simple sets of group convolutional neural networks. In particular, we find that for infinite groups and appropriately chosen convolutional kernels, already two-parameter families of convolutional neural networks have an ...
arxiv