(한국어로 답변주실 수 있다면 한국어로 답변 부탁드립니다.)
I am currently designing a model using the Deep Network Designer to implement a Convolutional Block Attention Module (CBAM).
For this, I need to use a channel-wise max/average pooling layer, and I am wondering if there is a way to implement this within MATLAB.
Additionally, I would like to know if there is a method to share a fully connected layer across different parts of the network. The current implementation of the fully connected layer seems to be limited to a single input and output.
Lastly, when passing through the fully connected layer, the dimension of the output is converted to (C x B). Is there a way to reshape this back to (S x S x C x B) in order to perform element-wise multiplication?(한국어로 답변주실 수 있다면 한국어로 답변 부탁드립니다.)
I am currently designing a model using the Deep Network Designer to implement a Convolutional Block Attention Module (CBAM).
For this, I need to use a channel-wise max/average pooling layer, and I am wondering if there is a way to implement this within MATLAB.
Additionally, I would like to know if there is a method to share a fully connected layer across different parts of the network. The current implementation of the fully connected layer seems to be limited to a single input and output.
Lastly, when passing through the fully connected layer, the dimension of the output is converted to (C x B). Is there a way to reshape this back to (S x S x C x B) in order to perform element-wise multiplication? (한국어로 답변주실 수 있다면 한국어로 답변 부탁드립니다.)
I am currently designing a model using the Deep Network Designer to implement a Convolutional Block Attention Module (CBAM).
For this, I need to use a channel-wise max/average pooling layer, and I am wondering if there is a way to implement this within MATLAB.
Additionally, I would like to know if there is a method to share a fully connected layer across different parts of the network. The current implementation of the fully connected layer seems to be limited to a single input and output.
Lastly, when passing through the fully connected layer, the dimension of the output is converted to (C x B). Is there a way to reshape this back to (S x S x C x B) in order to perform element-wise multiplication? deep learning, cbam, channel-wise pooling layer, reshape layer MATLAB Answers — New Questions

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