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  • What does 1x1 convolution mean in a neural network?
    $\begingroup$ 1x1 conv creates channel-wise dependencies with a negligible cost This is especially exploited in depthwise-separable convolutions This is especially exploited in depthwise-separable convolutions
  • 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
  • In CNN, are upsampling and transpose convolution the same?
    Both the terms "upsampling" and "transpose convolution" are used when you are doing "deconvolution" ( lt;-- not a good term, but let me use it here) Originally, I thought that they mean the same t
  • Convolutional Layers: To pad or not to pad? - Cross Validated
    Quote from Stanford lectures: "In addition to the aforementioned benefit of keeping the spatial sizes constant after CONV, doing this actually improves performance 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
  • Difference between Conv and FC layers? - Cross Validated
    (Note that each conv layer usually learns a set of several filters, each of which gets applied repeatedly across the input E g if the conv layer learns 16 different features, it is said to have a 'depth' of 16 ) FC layers are used to detect specific global configurations of the features detected by the lower layers in the net
  • definition of hidden unit in a ConvNet - Cross Validated
    A 'unit' to me is a single output from a single layer So if you have a conv layer, and it's not the output layer of the network, and let's say it has 16 feature planes (otherwise known as 'channels'), and the kernel is 3 by 3; and the input images to that layer are 128x128, and the conv layer has padding so the output images are also 128x128
  • 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 nets being (batch, steps, channels) They are both used for tasks involving sequences like time series, NLP etc So my question here is this,
  • How to calculate the Transposed Convolution? - Cross Validated
    $\begingroup$ The math formula is the one you wrote (check bounds), i e convolution, where the kernel is mirrored across x and y axes and swiped over the image
  • Why is max pooling necessary in convolutional neural networks?
    If you instead assume A: conv (stride=1) + max pooling replaced by B: conv (stride=2) things become different (B is then faster of course) $\endgroup$ – robintibor Commented May 18, 2020 at 11:50





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