The deep convolution and conventional convolution kernel are included in DO-Conv. ∘ means the depthwise convolution operator and * means convolution operator.</p
A dilated casual convolution with dilated factors d = 1,2,4 and filter kernel size k = 3.</p
(a) Single Scale Regular Convolution (b), Multi-Scale Regular Convolution, and (c) Multi-Scale Depth...
The development of multi-core computers means that the characteristics of digital filters can be rap...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
It consists of the feature extraction subnetwork and cross-correlation operation. The feature extrac...
In convolution operator, k, s, p, and c stand for kernel, stride, padding, and the number of output ...
Both operations use a 3x3 kernel and a stride of two. Traditional convolution determines the output ...
Image shows the deep-learning convolutional neural network architecture used in this study.</p
The left, middle, and right sides represent standard convolution, deformable convolution, and T-defo...
Computing discrete two-dimensional convolutions is an important problem in image processing. In mat...
International audienceThis work introduces a new unsupervised representation learning technique call...
The architecture of the convolutional neural network with corresponding kernel size (k), number of f...
The input image has no effect on the characteristics of the convolution layer, n is a fixed value.</...
14.14.0 (2020-08-07) Features Convolution: Allows 2D convolution kernels (#1549) (824b3d7
<p>The stereotypical peak used as the deconvolution kernel to identify potential peaks is shown.</p
A dilated casual convolution with dilated factors d = 1,2,4 and filter kernel size k = 3.</p
(a) Single Scale Regular Convolution (b), Multi-Scale Regular Convolution, and (c) Multi-Scale Depth...
The development of multi-core computers means that the characteristics of digital filters can be rap...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
It consists of the feature extraction subnetwork and cross-correlation operation. The feature extrac...
In convolution operator, k, s, p, and c stand for kernel, stride, padding, and the number of output ...
Both operations use a 3x3 kernel and a stride of two. Traditional convolution determines the output ...
Image shows the deep-learning convolutional neural network architecture used in this study.</p
The left, middle, and right sides represent standard convolution, deformable convolution, and T-defo...
Computing discrete two-dimensional convolutions is an important problem in image processing. In mat...
International audienceThis work introduces a new unsupervised representation learning technique call...
The architecture of the convolutional neural network with corresponding kernel size (k), number of f...
The input image has no effect on the characteristics of the convolution layer, n is a fixed value.</...
14.14.0 (2020-08-07) Features Convolution: Allows 2D convolution kernels (#1549) (824b3d7
<p>The stereotypical peak used as the deconvolution kernel to identify potential peaks is shown.</p
A dilated casual convolution with dilated factors d = 1,2,4 and filter kernel size k = 3.</p
(a) Single Scale Regular Convolution (b), Multi-Scale Regular Convolution, and (c) Multi-Scale Depth...
The development of multi-core computers means that the characteristics of digital filters can be rap...
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