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 …
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 …
Calculating Convolution Only for a Certain Interval Using "conv()" …
Sep 25, 2021 · The convolution is calculated using 2 methods. In one of them I use the built-in function conv() and in the other I use the definition of the convolution. In mat_conv1 and …
What does 1x1 convolution mean in a neural network? (v2)
Dec 21, 2018 · Looking at YOLO architecture (for example here), however, we can see that there are some 1x1xN conv layers applied after a MxMxN conv layer, i.e. the dimensionality is not …
Difference between Conv and FC layers? - Cross Validated
Nov 9, 2017 · (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 …
machine learning - RNN vs Convolution 1D - Cross Validated
Aug 15, 2018 · 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, …
In CNN, are upsampling and transpose convolution the same?
Sep 24, 2019 · Both the terms "upsampling" and "transpose convolution" are used when you are doing "deconvolution" (<-- not a good term, but let me use it here). Originally, I thought that …
What is MBConv that EfficientNetv2 is using? - Cross Validated
Apr 5, 2021 · EfficinetNetV2 uses MBConv/Fused-MBConv as a part of it's architecture. There is no clarity of what these operations actually are from the paper (nor from the references). It …
machine learning - How to convert fully connected layer into ...
Feb 22, 2017 · In this example, as far as I understood, the converted CONV layer should have the shape (7,7,512), meaning (width, height, feature dimension). And we have 4096 filters. And …
How is RELU used on convolutional layer - Cross Validated
Apr 25, 2019 · $\begingroup$ 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 …