Sep 25, 2021 · The convolution is calculated using 2 methods. In one of them I use the built-in function conv() and in the other I use the definition of the convolution. In mat_conv1 and cont_conv1 the …

Jul 31, 2017 · 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 …

Apr 17, 2020 · A 1-by-1 convolutional layer can (e.g.) be used to reduce the number of operations between two conv. layers. Example: applying a $5 \times 5 \times 32$ conv. with same padding onto …

Sep 23, 2020 · I am trying to think of scenarios where a fully connected (FC) layer is a better choice than a convolution layer. In terms of time complexity, are they the same? I know that convolution can …

Apr 27, 2018 · I'm having some difficulty in interpreting the functional model layers in keras: Does the code below mean we are doing 2 convolutions before max pooling? If so, why are we doing it twice …

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 …

1x1 conv creates channel-wise dependencies with a negligible cost. This is especially exploited in depthwise-separable convolutions. Nobody said anything about this but I'm writing this as a comment …

Feb 25, 2020 · One version of what you described is called Inception layer, outlined in Going Deeper with Convolutions paper. Other than that, if you want to concatenate conv layers with different …

Mar 13, 2018 · Generally speaking, I think for conv layers we tend not to focus on the concept of 'hidden unit', but to get it out of the way, when I think 'hidden unit', I think of the concepts of 'hidden' and …

Dec 29, 2023 · I am trying to understand the neural network architecture used by Ho et al. in "Denoising Diffusion Probabilistic Models" (paper, source code). They include self …