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Hi,
(1) Critic loss curve which should go to 0 will be including gradient penalty or without it?
(2) What should be the behavior of gradient penalty(Decreasing towards 0 or something else)?
(3) The result will be the same if we do backward propagation of gradient penalty individual or with discriminator loss as below.
(i) gradient_penalty.backward(retain_graph=True) [ Individual ]
(ii) loss_D = (- loss_real + loss_fake) + gradient_penalty [ with discriminator loss ]
loss_D.backward()
The text was updated successfully, but these errors were encountered:
Hi,
(1) Critic loss curve which should go to 0 will be including gradient penalty or without it?
(2) What should be the behavior of gradient penalty(Decreasing towards 0 or something else)?
(3) The result will be the same if we do backward propagation of gradient penalty individual or with discriminator loss as below.
(i) gradient_penalty.backward(retain_graph=True) [ Individual ]
(ii) loss_D = (- loss_real + loss_fake) + gradient_penalty [ with discriminator loss ]
loss_D.backward()
The text was updated successfully, but these errors were encountered: