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 …
Convolutional Layers: To pad or not to pad? - Cross Validated
AlexNet architecture uses zero-paddings as shown in the pic. However, there is no explanation in the paper why this padding is introduced. Standford CS 231n course teaches we use padding …
What is the difference between Conv1D and Conv2D?
Jul 31, 2017 · I was going through the keras convolution docs and I have found two types of convultuion Conv1D and Conv2D. I did some web search and this is what I understands about …
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 …
machine learning - Why does each convolution layer require …
Mar 1, 2019 · Well, you can have a network with only one conv layer, although this wouldn't be useful for much. You can also have a network composed of only conv layers. You can even …
Where should I place dropout layers in a neural network?
Oct 14, 2016 · I've updated the answer to clarify that in the work by Park et al., the dropout was applied after the RELU on each CONV layer. I do not believe they investigated the effect of …
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 …
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 …
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 …
How to convert fully connected layer into convolutional layer?
Feb 22, 2017 · At the second converted Conv layer (converted from FC2), we have 4096 * (1,1,4096) filters, and they give us a output vector (1,1,4096). It's very important for us to …