ScoreCAM paper Algorithm1 implementation question

[CHILLQQ]

Hello,

I have a question about the implementation of the Algorithm1 of the ScoreCAM paper. The code

              # how much increase if keeping the highlighted region
              # predication on masked input
              output = self.model_arch(input * norm_saliency_map)
              output = F.softmax(output)
              score = output[0][predicted_class]

suggests that the output is simply the masked images run through the original neural net. However, in the paper there is an additional step: $S^{c} = f^c(M) - f^c(X_b)$.

I am not sure exactly why this step is needed in the first place, but since it is in the paper, I am curious why it does not seem to be in the code?

Thank you.

[usamahassan965]

Just comparing with baseline . But if you see the methodology diagram , this step is not done there.