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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
  • Extract features with CNN and pass as sequence to RNN
    But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images
  • What is the fundamental difference between CNN and RNN?
    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
  • In a CNN, does each new filter have different weights for each input . . .
    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
  • convolutional neural networks - When to use Multi-class CNN vs. one . . .
    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
  • 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
  • neural networks - Are fully connected layers necessary in a CNN . . .
    A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN) See this answer for more info An example of an FCN is the u-net, which does not use any fully connected layers, but only convolution, downsampling (i e pooling), upsampling (deconvolution), and copy and crop operations
  • 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
  • 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





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