convolution


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Related to convolution: Fourier transform, Convolution theorem
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  • noun

Synonyms for convolution

Synonyms for convolution

the shape of something rotating rapidly

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the action of coiling or twisting or winding together

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References in periodicals archive ?
For example, DeepPose [3] is the first CNNs proposed to solve human pose estimation problem with cascade convolution network regressed by Toshev et al.
Finally, the last layer performs a transposed convolution that increases the dimensions to (H, W, CO), whose height and width are the same as the input image, with the depth in each pixel being the likelihood that the pixel belongs to each of the CO classes.
The VSoC concept of column-parallel, charge-based signal processing and flexible ASIP-based control enables 1D analogue convolution operations and software-defined digitisation.
Moreover, a useful mathematical convolution was discovered for a study of international behaviors regarding beverage consumption (Lanier, 2013).
To capture more spatial information and local rain drops, we propose a multiscale parallel convolution generator network which consists of two-branch convolution operator with different kernel sizes.
Therefore, using MKNet-C with one more convolution block conv3, it is expected to improve the accuracy [14].
Then, convolution and sampling are made again to the output features of sampling layer, thus forming a kind of layered feature extractor and output of each layer can be regarded as an advanced expression form (i.e., advanced feature) of the "original" voice signal.
The first 13 layers constitute an encoder network that performs the convolution with 64 filter banks of size 3 x 3 to obtain sets of features along with batch normalization in a minibatch set of 8 images.
The deep CNN is a feedforward NN that comprises a stack of alternating convolution layers and pooling layers and then fully connected layers and softmax layers [13] (Figure 2).
Although amounts of pooling operations enlarge the receptive fields of the convolution kernel of FCN, they lose the detailed location information, resulting in coarse segmentation result, which hinders its further application.
[8], and a method of discrete convolution and FFT (DCFFT) was advanced, which completely avoids any additional error except for the discretization error.
In the current work, the SEM image of cemented backfill captured at the microscopic scale is regarded as the input sample in the deep convolution neural network.
In order to solve these problems, we propose a multiscale deep convolution detection network to detect small objects.
The Hadamard product or convolution of two power series [h.sub.1](z) = [[summation].sup.[infinity].sub.n=1] [a.sub.n][z.sup.n] and [h.sub.2](z) = [[summation].sup.[infinity].sub.n=1] [c.sub.n][z.sup.n] is given by [h.sub.1](z) * [h.sub.2](z) = ([h.sub.1] * [h.sub.2])(z) = [[summation].sup.[infinity].sub.n=1] [a.sub.n][c.sub.n][z.sup.n].
As the convolution layer is the most complicated part of CNN, we first optimize the single convolution layer and then, on this basis, we accelerate complete CNN and explore different optimization strategies.