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machine learning - What is a fully convolution network? - Artificial . . . Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
What is the fundamental difference between CNN and RNN? 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
How is the depth of a convolutional layer determined? The 96 96 is the number of feature maps, which is equal to the number of filters kernels The choice of the number of kernels is not fully arbitrary, although there is no equation or exact rule restricting the number If you have a CNN, one single convolution operation would be pointless: since it is used for the whole image information, it can generalize, but only to specific (meaning: a
What is a cascaded convolutional neural network? The paper you are citing is the paper that introduced the cascaded convolution neural network In fact, in this paper, the authors say 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
deep learning - Artificial Intelligence Stack Exchange Why do we need convolutional neural networks instead of feed-forward neural networks? What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classificat
convolutional neural networks - What is the meaning of a 2D stride . . . In TensorFlow is common to see 4D stride parameters, with axes of (example, height, width, channel) Typically you don't want to skip any examples or channels in a normal CNN, even though the framework can do that, so you will see values like (1, 2, 2, 1) used when using stride to downsample just image height and width dimensions between layers