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View Code? Open in Web Editor NEWTensorflow implementation of 'Visual Feature Attribution using Wasserstein GANs'
License: GNU General Public License v3.0
Tensorflow implementation of 'Visual Feature Attribution using Wasserstein GANs'
License: GNU General Public License v3.0
Hi,
I have a question regarding gradient penalty. I believe the L2-norm calculation of the gradient is incorrect. It should be sum across all axes except the batch axis because the norm of a matrix should be a scalar value. However, your L2-norm calculation returns a matrix not a scalar value. Because you are summing across the channel axis only. Is there a special reason for that?
vagan-code/vagan/model_vagan.py
Line 134 in 6be0b51
Best Regards,
Cem
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