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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
  • convolutional neural networks - keras model accuracy not improving . . .
    I am trying to do multi class(16) classification, however no matter what parameters or number of layers I use my accuracy is not improving, its in 30s the max I got was 43 I have tried early stopp
  • 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 are acting as weights in a convolution neural network?
    In a CNN, the weights are the kernels filters of the CNN, i e the matrices that you use to perform the convolution (or cross-correlation) operation in a convolutional layer
  • 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
  • How is the depth of the filters of convolutional layers determined . . .
    I am a bit confused about the depth of the convolutional filters in a CNN At layer 1, there are usually about 40 3x3x3 filters Each of these filters outputs a 2d array, so the total output of the
  • When training a CNN, what are the hyperparameters to tune first?
    I am training a convolutional neural network for object detection Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I r


















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