You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi, thanks for your nice job! I have some questions about the paper:
1、I try to reproduce the results of the Table 3 in paper:
I train the model PreAct-ResNet-18 generates the seen adversarial samples with l∞ of ε = 8/255 in training time for training a robust model and also generates the unseen adversarial samples with different sized l∞ balls and other types of norm ball, e.g., l1, l2 for testing the robustness of the model with "unseen attacks". However, I find that the defense model trained with l∞ of ε = 8/255 achieves the better performance on the adversarial samples (generated by the trained defense model ) with l2 of ε = 300/255 than the results in paper, e.g. the accuracy on adversarial samples with l2 of ε = 300/255 is 38.48% (36.87% in Table 3 of the paper) only in 5th epoch. I want to know whether there is a problem in my generation of the unseen adversarial samples with l2 of ε = 300/255 and lead to the fake better results than the paper?
2、Whether the defense model in the Table 3 is trained on PGD 100 with l∞ of ε = 8/255? It seems that there is no related descriptions about it.
I would be grateful if you can help me with the above puzzles. Thank you!
The text was updated successfully, but these errors were encountered:
Hi, thanks for your nice job! I have some questions about the paper:
1、I try to reproduce the results of the Table 3 in paper:
I train the model PreAct-ResNet-18 generates the seen adversarial samples with l∞ of ε = 8/255 in training time for training a robust model and also generates the unseen adversarial samples with different sized l∞ balls and other types of norm ball, e.g., l1, l2 for testing the robustness of the model with "unseen attacks". However, I find that the defense model trained with l∞ of ε = 8/255 achieves the better performance on the adversarial samples (generated by the trained defense model ) with l2 of ε = 300/255 than the results in paper, e.g. the accuracy on adversarial samples with l2 of ε = 300/255 is 38.48% (36.87% in Table 3 of the paper) only in 5th epoch. I want to know whether there is a problem in my generation of the unseen adversarial samples with l2 of ε = 300/255 and lead to the fake better results than the paper?
2、Whether the defense model in the Table 3 is trained on PGD 100 with l∞ of ε = 8/255? It seems that there is no related descriptions about it.
I would be grateful if you can help me with the above puzzles. Thank you!
The text was updated successfully, but these errors were encountered: