Closed scott870430 closed 2 years ago
Hi @scott870430, thanks for your interest in our work!
We calculated the AUC using
np.trapz(y, x) * 100
Which is not equivalent to the average.
Also, we accounted for the original accuracy as well, i.e. for x=0 we used the accuracy of the network.
This is my full table of results for rollout for example (negative perturbation, top class):
I hope this helps. Best, Hila.
Thank you for the response!
Hi @hila-chefer! I have a question about perturbation results, following your README.md, I try to reproduce the perturbation results. Is the average of np.mean(num_correct_pertub, axis=1) AUC? There is no false negative in this experiment, so the y-axis is the classification accuracy, and the x-axis is the different cover rate (0.1~0.9)? I got a little different result on the rollout method for 55.2 (53.1 in paper), and GradCAM for 41.8 (41.52 in paper) with
--vis-class = top
. Are my reproduced results and understanding correct?Thank you in advance for your help.