What does 1x1 convolution mean in a neural network?
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 …
What is the difference between Conv1D and Conv2D?
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 …
Batch normalization and the need for bias in neural networks
Aug 10, 2020 · I've read that batch normalization eliminates the need for a bias vector in neural networks, since it introduces a shift parameter that functions similarly as a bias. As far as I'm aware …
neural networks - Difference between strided and non-strided ...
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 …
How do bottleneck architectures work in neural networks?
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...
How to calculate the Transposed Convolution? - Cross Validated
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 ...
Difference between Conv and FC layers? - Cross Validated
Nov 9, 2017 · What is the difference between conv layers and FC layers? Why cannot I use conv layers instead of FC layers?
In CNN, are upsampling and transpose convolution the same?
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 …
What are the advantages of FC layers over Conv layers?
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 …
What does 1x1 convolution mean in a neural network? (v2)
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.