For example, DeepPose  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 .
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  (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.
, 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
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.