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A CNN, in specific, has one or more layers of convolution units. A convolution unit receives its input from multiple units from the previous layer which together create a proximity. Therefore, the input units (that form a small neighborhood) share their weights. The convolution units (as well as pooling units) are especially beneficial as:
A fully convolutional network is achieved by replacing the parameter-rich fully connected layers in standard CNN architectures by convolutional layers with $1 \times 1$ kernels. I have two questions. What is meant by parameter-rich? Is it called parameter rich because the fully connected layers pass on parameters without any kind of "spatial ...
A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems. CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text and speech analysis.
Firstly when you say an object detection CNN, there are a huge number of model architectures available. Considering that you have narrowed down on your model architecture a CNN will have a few common layers like the ones below with hyperparameters you can tweak: Convolution Layer:- number of kernels, kernel size, stride length, padding
To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the introduction of a new input feature which fulfills the "cascade manner" and "convolution manner" simultaneously (see Sec. 3.2) and a new cost function which can model the ...
This is the same thing as in CNNs. The only difference is that, in CNNs, the kernels are the learnable (or trainable) parameters, i.e. they change during training so that the overall loss (that the CNN is making) reduces (in the case CNNs are trained with gradient descent and back-propagation).
In a CNN (such as Google's Inception network), bottleneck layers are added to reduce the number of feature maps (aka channels) in the network, which, otherwise, tend to increase in each layer. This is achieved by using 1x1 convolutions with fewer output channels than input channels.
Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel. There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel. So the diagrams showing one set of weights per input channel for each filter are correct.
You can use CNN on any data, but it's recommended to use CNN only on data that have spatial features (It might still work on data that doesn't have spatial features, see DuttaA's comment below). For example, in the image, the connection between pixels in some area gives you another feature (e.g. edge) instead of a feature from one pixel (e.g ...
However, I stumbled on this question while looking how to do variable size image inputs for a CNN. Variable size inputs are indeed possible for a convolutional approach - albeit with some caveats, and the stats.stackexchange link above explores that related, alternate line of inquiry. $\endgroup$ –