Results 41 to 50 of about 1,012,747 (224)
Frequency-Adaptive Dilated Convolution for Semantic Segmentation [PDF]
Computer Vision and Pattern RecognitionDilated convolution, which expands the receptive field by inserting gaps between its consecutive elements, is widely employed in computer vision. In this study, we propose three strategies to improve individual phases of dilated convolution from the ...
Linwei Chen, Lin Gu, Ying Fu
semanticscholar +1 more source
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions [PDF]
Computer Vision and Pattern Recognition, 2020 Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e.
Ricard Durall +2 more
semanticscholar +1 more source
Topological convolution algebras [PDF]
, 2013 In this paper we introduce a new family of topological convolution algebras of the form $\bigcup_{p\in\mathbb N} L_2(S,\mu_p)$, where $S$ is a Borel semi-group in a locally compact group $G$, which carries an inequality of the type $\|f*g\|_p\le A_{p,q}\|
Alpay, Daniel, Salomon, Guy
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LSTM-CNN Architecture for Human Activity Recognition
IEEE Access, 2020 In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance.
Kun Xia, Jianguang Huang, Hanyu Wang
doaj +1 more source
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks [PDF]
IEEE International Conference on Computer Vision, 2019 As designing appropriate Convolutional Neural Network (CNN) architecture in the context of a given application usually involves heavy human works or numerous GPU hours, the research community is soliciting the architecture-neutral CNN structures, which ...
Xiaohan Ding +3 more
semanticscholar +1 more source
Convergence of random walks on double transitive group generated by its permutational character
Visnik Harkivsʹkogo Nacionalʹnogo Universitetu im. V.N. Karazina. Cepiâ Matematika, Prikladna Matematika i Mehanika, 2019 Let $P$ be a probability on a finite group $G$, $U(g)=\frac{1}{|G|}$ the uniform (trivial) probability on the group $G$, $P^{(n)}=P *\ldots*P$ an $n$-fold convolution of $P$.
Aleksandr L. Vyshnevetskiy
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Random convolution ensembles [PDF]
, 2007 A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The
Mayo, Michael
core +2 more sources
Harmonic analysis on Heisenberg--Clifford Lie supergroups
, 2011 We define a Fourier transform and a convolution product for functions and distributions on Heisenberg--Clifford Lie supergroups. The Fourier transform exchanges the convolution and a pointwise product, and is an intertwining operator for the left regular
Alldridge, Alexander +2 more
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Non-Schlesinger Isomonodromic Deformations of Fuchsian Systems and Middle Convolution [PDF]
, 2015 The paper is devoted to non-Schlesinger isomonodromic deformations for resonant Fuchsian systems. There are very few explicit examples of such deformations in the literature.
Bibilo, Yulia, Filipuk, Galina
core +4 more sources
SDAUNet: A simple dual attention mechanism UNet for mixed noise removal
IET Image Processing, 2023 Convolutional neural networks (CNNs) have demonstrated impressive results in additive white Gaussian noise removal due to their strong fitting ability.
Jielin Jiang +4 more
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