<p>Suppose I have a feature map with size <span class="math-container">$C_1 \times H \times W$</span>. And I need to convert it into a feature map of size <span class="math-container">$C_2 \times H \times W$</span>.</p>
<p>One way to do this is to use convolutional neural networks as <a href="https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html" rel="nofollow noreferrer">Conv2d(<span class="math-container">$C_1, C_2$</span>)</a></p>
<p>I want to know whether there are any other ways in Literature to perform the desired operation?</p>

CC BY-SA 4.0
2021-10-12T07:54:21.593Z
any specific goal in mind when thinking about resizing these feature maps? from the way the question is phrased now it seems you're asking for a generic list that potentially include any matrix operation
Edoardo Guerriero
CC BY-SA 4.0
2021-10-12T08:47:11.287Z
@EdoardoGuerriero Goal is to down-sample only, but without learning parameters.
hanugm
<channel><feature-maps>
What are the recommended ways to change shape of feature maps channel wise other than using Convolutional neural networks?
hanugm
# What are the recommended ways to change shape of feature maps channel wise other than using Convolutional neural networks?

(Asked by: username_0 on 2021-10-11)

Suppose I have a feature map with size $C\_1 \times H \times W$. And I need to convert it into a feature map of size $C\_2 \times H \times W$.

One way to do this is to use convolutional neural networks as [Conv2d($C\_1, C\_2$)](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)

I want to know whether there are any other ways in Literature to perform the desired operation?

**Tags:** channel, feature-maps

> **Comments:**
> **username_1**: any specific goal in mind when thinking about resizing these feature maps? from the way the question is phrased now it seems you're asking for a generic list that potentially include any matrix operation  
> **username_0**: @EdoardoGuerriero Goal is to down-sample only, but without learning parameters.  

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## 0 answers

