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    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
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    0 I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN
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    CNN vs 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 In a very general way, a CNN will learn to recognize components of an image (e g , lines, curves, etc ) and then learn to combine these components
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    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
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    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
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    I think the squared image is more a choice for simplicity There are two types of convolutional neural networks Traditional CNNs: CNNs that have fully connected layers at the end, and fully convolutional networks (FCNs): they are only made of convolutional layers (and subsampling and upsampling layers), so they do not contain fully connected layers With traditional CNNs, the inputs always need
  • Reduce receptive field size of CNN while keeping its capacity?
    One way to keep the capacity while reducing the receptive field size is to add 1x1 conv layers instead of 3x3 (I did so within the DenseBlocks, there the first layer is a 3x3 conv and now followed by 4 times a 1x1 conv layer instead of the original 3x3 convs (which increase the receptive field)) In doing that, the number of parameters can be kept at a similar level While 1x1 convolutions are





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