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 …
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 does applying a 1-by-1 convolution (bottleneck layer) between …
Apr 17, 2020 · A 1-by-1 convolutional layer can (e.g.) be used to reduce the number of operations between two conv. layers. Example: applying a $5 \times 5 \times 32$ conv. with same padding onto …
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 adding dropout …
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 …
Pooling vs. stride for downsampling - Cross Validated
Jan 16, 2019 · Pooling and stride both can be used to downsample the image. Let's say we have an image of 4x4, like below and a filter of 2x2. Then how do we decide whether to use (2x2 pooling) vs. …
How to obtain the last convolutional layer of a model in torchvision ...
Oct 21, 2023 · I'm not sure, because the last convolutional layer can vary in each model. And my main concern is regarding which is the last convolutional layer of the efficient net b0.