In the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network by Christian Ledig et al., the distance between images (used in the loss function) is calculated from feature maps $\phi_{i,j}$ extracted from the VGG19 network,
where $\phi_{i,j}$ is defined as "feature map obtained by the j-th convolution (after activation) before the i-th maxpooling layer".
Can you elaborate on how to calculate this feature map, may be for VGG54 mentioned in the paper?
$\phi_{5,4}$ means 4th convolutional layer before 5th max-pooling layer right? But 4th layer has so 512 filters. So we would have 512 feature spaces. Which one to choose from this? Also what does "after activation" mean?
I found this answer related to the same issue, but the answer didn't explain much.