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 …

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 …

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 …

We define a bottleneck architecture as the type found in the ResNet paper where [two 3x3 conv layers] are replaced by [one 1x1 conv, one 3x3 conv, and another 1x1 conv layer]. I understand that t...

Nov 9, 2017 · What is the difference between conv layers and FC layers? Why cannot I use conv layers instead of FC layers?

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 ...

Jul 16, 2017 · Typical-looking activations on the first CONV layer (left), and the 5th CONV layer (right) of a trained AlexNet looking at a picture of a cat. Every box shows an activation map corresponding to …

Dec 21, 2018 · Most of the answers to that question indicated how 1x1 conv layers are used for dimensionality reduction (or in general, a dimensionality change) in the filter dimension.

Dec 24, 2022 · Below you can see a convolutional network with 24 convolutional layers. I am trying to understand the shape of the network. Given the input image with shape 448x448x3, we apply first …

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 …