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max-liu-112 / SRGANs-Spectral-Regularization-GANs-

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Confusing naming #1

Open veqtor opened 5 years ago

veqtor commented 5 years ago

While the paper is about spectral regularization, all of the code maintains the SN / Spectral Normalization naming, it would be nice, just for clarity if it was changed to SR / Spectral Regularization.

mgirardot commented 5 years ago

It seems that the modifications are not pushed to the repo yet. I don't see any operation related to the equations in the paper. Maybe after ICCV2019 ? Very interesting paper.

max-liu-112 commented 5 years ago

hi, Michael, Thank you for you contacting. The following link https://github.com/max-liu-112/SRGANs is our proposed SRGANs, training on ImageNet. Actually, few modification need to be made based on SNGANs. Specifically, the ‘updated.py’ involves the sr method, using dynamic compensation. Dynamic compensation is introduced in the latest version https://arxiv.org/pdf/1908.10999.pdf.

Michael Girardot notifications@github.com于2019年10月31日 周四下午8:16写道：

It seems that the modifications are not pushed to the repo yet. I don't see any operation related to the equations in the paper. Maybe after ICCV2019 ? Very interesting paper.

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mgirardot commented 5 years ago

Great, thank you for the clarification. I could not find the SR specific code. Could you maybe add a pointer in the readme ?

max-liu-112 commented 5 years ago

ok， I will do it later. Now I am out for dinner.

Michael Girardot notifications@github.com于2019年10月31日 周四下午10:04写道：

Great, thank you for the clarification. I could not find the SR specific code. Could you maybe add a pointer in the readme ?

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max-liu-112 commented 5 years ago

We update the read.md. We hope it works fine for you. If any more questions, please contact me. Thanks

Michael Girardot notifications@github.com于2019年10月31日 周四下午10:04写道：

Great, thank you for the clarification. I could not find the SR specific code. Could you maybe add a pointer in the readme ?

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mgirardot commented 5 years ago

Thank you. I am following the equations in the paper and the code to try to adapt it to my code base (Keras/tensorflow).