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  • What does 1x1 convolution mean in a neural network?
    Suppose that I have a conv layer which outputs an (N, F, H, W) shaped tensor where: N is the batch size F is the number of convolutional filters H, W are the spatial dimensions Suppose the input is fed into a conv layer with F1 1x1 filters, zero padding and stride 1 Then the output of this 1x1 conv layer will have shape (N, F1, H, W) So 1x1 conv filters can be used to change the
  • What is the difference between Conv1D and Conv2D?
    I will be using a Pytorch perspective, however, the logic remains the same When using Conv1d (), we have to keep in mind that we are most likely going to work with 2-dimensional inputs such as one-hot-encode DNA sequences or black and white pictures The only difference between the more conventional Conv2d () and Conv1d () is that latter uses a 1-dimensional kernel as shown in the picture
  • Convolutional Layers: To pad or not to pad? - Cross Validated
    If the CONV layers were to not zero-pad the inputs and only perform valid convolutions, then the size of the volumes would reduce by a small amount after each CONV, and the information at the borders would be “washed away” too quickly " -
  • Keras Functional model for CNN - why 2 conv layers?
    Keras Functional model for CNN - why 2 conv layers? Ask Question Asked 7 years, 1 month ago Modified 7 years, 1 month ago
  • In CNN, are upsampling and transpose convolution the same?
    Both the terms "upsampling" and "transpose convolution" are used when you are doing "deconvolution" (<-- not a good term, but let me use it here) Originally, I thought that they mean the same t
  • machine learning - RNN vs Convolution 1D - Cross Validated
    Intuitively, are both RNN and 1D conv nets more or less the same? I mean the input shape for both are 3-D tensors, with the shape of RNN being ( batch, timesteps, features) and the shape of 1D conv
  • Where should I place dropout layers in a neural network?
    I've updated the answer to clarify that in the work by Park et al , the dropout was applied after the RELU on each CONV layer I do not believe they investigated the effect of adding dropout following max pooling layers
  • How to convert fully connected layer into convolutional layer?
    It's mentioned in the later part of the post that we need to reshape the weight matrix of FC layer to CONV layer filters But I am still confusing about how to actually implement it Any explanation or link to other learning resource would be welcome
  • What are the advantages of FC layers over Conv layers?
    As mentioned in the article, convolutional layers are optimized for translationally-invariant parameters, such as pixel intensities in images and video If your parameters represent a discretized sample of a continuous variable, such as space or time, then translational invariance means that every window of the parameters (such as a 10x10 pixel slice of the image) is to some extent similar to





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