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 …

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 …

It seems to me the most important reason is to preserve the spatial size. As you said, we can trade-off the decrease in spatial size by removing pooling layers. However many recent network structures …

Sep 24, 2019 · It may depend on the package you are using. In keras they are different. Upsampling is defined here Provided you use tensorflow backend, what actually happens is keras calls tensorflow …

Aug 6, 2018 · conv = conv_2d (strides=) I want to know in what sense a non-strided convolution differs from a strided convolution. I know how convolutions with strides work but I am not familiar with the …

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 …

Sep 3, 2022 · Studying for my finals in Deep learning. I'm trying to solve the following question: Calculate the Transposed Convolution of input $A$ with kernel $K$: $$ A=\begin ...

Apr 25, 2019 · The answer that you might be looking for is that ReLU is applied element-wise (to each element individually) to the learned parameters of the conv layer ("feature maps").

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 …

Jan 16, 2019 · Pooling and stride both can be used to downsample the image. Let's say we have an image of 4x4, like below and a filter of 2x2. Then how do we decide whether to use (2x2 pooling) vs. …