Difference between "kernel" and "filter" in CNN
Dec 25, 2015 · What is the difference between the terms "kernel" and "filter" in the context of convolutional neural networks?
machine learning - What does kernel size mean? - Cross Validated
Aug 7, 2017 · When people talk about neural networks, what do they mean when they say "kernel size"? Kernels are similarity functions, but what does that say about kernel size?
What does 1x1 convolution mean in a neural network?
The convolution itself multiplies each pixel from the 3 channels with the corresponding coefficient and adds them together. This makes things more interesting: Essentially the 1x1 convolution has turned …
Do convolutional neural networks flip the kernel?
Jul 21, 2016 · After reading various examples of CNNs it doesn't look like the kernel used for convolution is flipped. Can anybody explain why?
When do we use an even size kernel in convolutional neural network …
Sep 13, 2018 · For an odd-size kernel, I know that its center is aligned with pixels in the image. For an even-size kernel, there is no such a center point, and I'm confused about how the kernel is combined …
Convolution with a non-square kernel - Cross Validated
Jun 15, 2018 · So far I've only encountered convolution kernels which are square (ie, have the same rows as columns). Are there any cases in which a non-square kernel makes sense? If not, why?
Is a 1D convolution of size $m$ with $k$ channels the same as a 2D ...
If the convolution kernel sweeps over 1 dimension, it is a 1D convolution, regardless of the number of channels or the dimension of the input tensor. If the convolution sweeps over 2 dimensions, it is a 2D …
Calculating the number of multiplications required for a 2d convolution
Dec 11, 2022 · Calculating the number of multiplications required for a 2d convolution Ask Question Asked 2 years, 11 months ago Modified 9 months ago
difference between the "Kernel Convolution" and "Kernel PCA"
Nov 12, 2020 · For kernel PCA, the "kernel" is the same as the concept for "kernel based methods" such as support vector machines. They are functions that can stand in for the vector dot-product (or …
Difference between strided and non-strided convolution
Aug 6, 2018 · Applying convolution means sliding a kernel over an input signal outputting a weighted sum where the weights are the values inside the kernel. The stride is the sliding step. You can not …