在连接深度特征向量和手工制作的特征向量后，我可以使用最终池化层来找到最佳共同特征吗？

Question

I have two features vector. One is deep features vector extracted by CNN and another is handcrafted features extracted by uniform local binary pattern. I want to find common best features after concatenating these two features vector. I want to use a final pooling layer for this reason. Is it possible?

Answer 1

After you have concatenated the two feature vectors, the final pooling layer would help in reducing those feature vectors.

If you can define more what you aim to do / which pooling layer do you want to use?

Answer 2

I'm not sure I understand correctly what you meant by "final pooling layer"

But in my opinion, adding ONLY a pooling layer after the concatenation layer and before the output layer (eg, Dense-softmax...) may not help much in this case as pooling layers have no learnable parameters, and they operate over each activation map independently to reduce the size of the activation maps.

There is one simple way of feature fusion methods I would like to suggest is that you can apply another subnet (set of layers like convolution, pooling, dense) to the concatenated tensor. Thus, the model can keep learning to enhance the good features.