Results 21 to 30 of about 1,031,506 (314)
Involution: Inverting the Inherence of Convolution for Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2021 Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel ...
Duo Li +7 more
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Diverse Branch Block: Building a Convolution as an Inception-like Unit [PDF]
Computer Vision and Pattern Recognition, 2021 We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single ...
Xiaohan Ding +3 more
semanticscholar +1 more source
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement [PDF]
Interspeech, 2020 Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on predicting TF-masks or speech spectrum, via a naive convolution neural network
Yanxin Hu +8 more
semanticscholar +1 more source
Understanding Convolution for Semantic Segmentation [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017 Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems.
Panqu Wang +6 more
semanticscholar +1 more source
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation [PDF]
Computer Vision and Pattern Recognition, 2020 State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution.
Xinge Zhu +7 more
semanticscholar +1 more source
Generalized convolutions V [PDF]
Studia Mathematica, 1973 This paper is a continuation of part III, ibid. 80, 167-189 (1984; Zbl 0561.60019). It deals with generalized convolution algebras (\({\mathcal P},\circ)\), i.e. \({\mathcal P}\) is the set of all probability measures on the positive half-line and ``\(\circ ''\) is a generalized convolution of measures from \({\mathcal P}\).
openaire +7 more sources
Convolution of n-dimensional Tempered Ultradistributions and Field Theory [PDF]
, 2003 In this work, a general definition of convolution between two arbitrary Tempered Ultradistributions is given. When one of the Tempered Ultradistributions is rapidly decreasing this definition coincides with the definition of J. Sebastiao e Silva.
Bollini, C. G., Rocca, M. C.
core +2 more sources
Convolutions, integral transforms and integral equations by means of the theory of reproducing kernels [PDF]
Opuscula Mathematica, 2012 This paper introduces a general concept of convolutions by means of the theory of reproducing kernels which turns out to be useful for several concrete examples and applications.
Luis P. Castro +2 more
doaj +1 more source
Convolutional Gated MLP: Combining Convolutions & gMLP
, 2021 Conference
Rajagopal, A., Nirmala, V.
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ASSOCIATIVE CONVOLUTIONS ARISING FROM CONDITIONALLY FREE CONVOLUTION [PDF]
Infinite Dimensional Analysis, Quantum Probability and Related Topics, 2005 We define two families of deformations of probability measures depending on the second free cumulants and the corresponding new associative convolutions arising from the conditionally free convolution. These deformations do not commute with dilation of measures, which means that the limit theorems cannot be obtained as a direct application of the ...
Krystek, Anna Dorota +1 more
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