Is this method relevant for binary classification? about agi

[pd90506]

This method is not specified to multi-class problems. In a binary case, there will be only 1 adversarial class, which leads this method to a form similar to IG, except that IG requires a straightline path and a starting reference point, this AGI method is still able to automatically find a path.

[leleogere]

Thank you for your answer, that is what I thought but I wasn't sure.

Anyway thank you for your work, I managed to get some pretty amazing results with this method that I could not get with classical IG.

One last question, is it normal that on the following lines the delta is not normalized?

AGI/AGI_main.py

Lines 89 to 90 in 4b28c8b

	grad_lab_norm = torch.norm(data_grad_lab,p=2)
	delta = epsilon * data_grad_adv.sign()

According to the algorithm below, I would expect something like delta = epsilon * (data_grad_adv / grad_lab_norm).sign() (especially as it seems that the variable grad_lab_norm is not used anywhere).

There could be an issue when the gradient is zero, but it could be solved by clipping the norm to some small value:

delta = epsilon * (data_grad_adv / torch.clamp(grad_lab_norm, min=1e-8)).sign()

[pd90506]

Thank you for your answer, that is what I thought but I wasn't sure.

Anyway thank you for your work, I managed to get some pretty amazing results with this method that I could not get with classical IG.

One last question, is it normal that on the following lines the delta is not normalized?

AGI/AGI_main.py

Lines 89 to 90 in 4b28c8b

	grad_lab_norm = torch.norm(data_grad_lab,p=2)
	delta = epsilon * data_grad_adv.sign()

According to the algorithm below, I would expect something like delta = epsilon * (data_grad_adv / grad_lab_norm).sign() (especially as it seems that the variable grad_lab_norm is not used anywhere).

There could be an issue when the gradient is zero, but it could be solved by clipping the norm to some small value:

delta = epsilon * (data_grad_adv / torch.clamp(grad_lab_norm, min=1e-8)).sign()

Yes! You're correct. But since we only take the sign, it shouldn't cause any differences. Your suggestion should also work.