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
{{ message }}
This repository has been archived by the owner on Nov 1, 2024. It is now read-only.
Dear Authors,
Thank you for making the paper and code open source. It is very helpful.
With respect to the image above - 2 steps are being done for adversarial learning - one term where the ld and lc are positive and the next point they are negative. Why not use a gradient reversal layer before the perturbation and covariate discriminator instead of the 2 step process, so that the loss can be back-propagated in a single forward and backward pass? Or this is just a design choice? I am just curious.
Am I missing something? Please let me know.
Thank you,
Megh
The text was updated successfully, but these errors were encountered:
Sign up for freeto subscribe to this conversation on GitHub.
Already have an account?
Sign in.
Dear Authors,

Thank you for making the paper and code open source. It is very helpful.
With respect to the image above - 2 steps are being done for adversarial learning - one term where the ld and lc are positive and the next point they are negative. Why not use a gradient reversal layer before the perturbation and covariate discriminator instead of the 2 step process, so that the loss can be back-propagated in a single forward and backward pass? Or this is just a design choice? I am just curious.
Am I missing something? Please let me know.
Thank you,
Megh
The text was updated successfully, but these errors were encountered: