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  • machine learning - What is a fully convolution network? - Artificial . . .
    An example of an FCN An example of a fully convolutional network is the U-net (called in this way because of its U shape, which you can see from the illustration below), which is a famous network that is used for semantic segmentation , i e classify pixels of an image so that pixels that belong to the same class (e g a person) are associated
  • Why can a fully convolutional network accept images of any size?
    The second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions Thus it is an end-to-end fully convolutional network (FCN), i e it only contains Convolutional layers and does not contain any Dense layer because of which it can accept image of any size
  • neural networks - Artificial Intelligence Stack Exchange
    FCN and FCNN are not the same, and I think you mean FCNN $\endgroup$ – Dave Commented Feb 17, 2023 at
  • 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
  • How to handle rectangular images in convolutional neural networks . . .
    However, in FCN, you don't flatten the last convolutional layer, so you don't need a fixed feature map shape, and so you don't need an input with a fixed size In both cases, you don't need a squared image You just have to be careful in the case you use CNN with a fully connected layer, to have the right shape for the flatten layer
  • What does downsampling and upsampling mean in coarse-to-fine . . .
    $\begingroup$ Moreover, in section 2 2 second paragraph, " the 3D FCN is trained on images of the lowest resolution in order to capture the largest amount of context, downsampled with a factor of ds1 = 2S and optimized using the Dice loss L1 In the next level, we use the predicted segmentation maps as a second input channel to the 3D FCN
  • Does a fully convolutional network share the same translation . . .
    The difference between an FCN and a regular CNN is that the former does not have fully connected layers See this answer for more info Therefore, FCNs inherit the same properties of CNNs There's nothing that a CNN (with fully connected layers) can do that an FCN cannot do
  • Wouldnt convolutional neural network models work better without . . .
    Read on Fully Convolutional Networks (FCN) There is a lot of papers on the subject, first was "Fully Convolutional Networks for Semantic Segmentation" by Long The idea is quite close to what you describe - preserve spatial locality in the layers In FCN there is no fully connected layer
  • In the DeepView paper, do they use the same FCN for all depth slices . . .
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • neural networks - FCNs: Questions about the filter rarefaction in the . . .
    I am reading the paper about the fully convolutional network (FCN) I had some questions about the part where the authors discuss the filter rarefaction technique (I guess this is roughly equivalent to dilated convolution) as a trick to compensate for the cost of implementing a shift-and-stich method





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