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[D]: Vanishing Gradients and Resnets
I am assuming you are doing a WGAN approach since that would explain the gradient penalty violation. In this case, use LayerNorm as indicated here: https://github.com/LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2/issues/3
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[D] Create Labels for Data created by a GAN
Thanks so much for the fast reply! I'm still fairly new and your help is highly appreciated! Let's have a look at the WGAN from here. How do I make that conditional? Do I just add a Layer at the beginning of module.ConvGenerator and module.ConvDiscriminator? Do I need to update anything else? Like the loss i.e. or the rest of the Layer? Does the resolution of the following layers change?
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Basic DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2 repo stats
2
399
1.5
over 2 years ago
LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2 is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2 is Python.
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