Comments (6)
I think it is not very meaningful. It is just the side effect of changing initial size 4x4 to 8x8.
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This implementation also alternates fade in and stabilizing stages.
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Ah I see. You took the min of 1 and the weight. Closing this now.
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On a second thought, in the ProGAN paper, it seems that they are doing stabilizing and then fading in. Here since we have alpha = min(1, 1 / args.phase * (used_sample + 1))
and used_sample
grows from 0 up, wouldn't it reverse the order (i.e. fade in first and then stabilize) ? I think this kind of makes a difference at the very first level of resolution. Thanks!
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Yes, it's different. But after initial resolution it will be same.
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Thanks! If I understand correctly, we start at 8x8, and at this time we fade in with the 4x4 right? My concern is that since the 4x4 is not yet learned (as we started from 8x8), would the fade-in at this stage be advantageous at all?
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Related Issues (20)
- Some confusing HOT 4
- .
- Image not visible properly and 512 model also giving output of (256,256) plz help HOT 1
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- generate new images from checkpoints HOT 2
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- Question on projecting generated images back to latent space HOT 2
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- Train script issue
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation HOT 5
- about the image size HOT 4
- training error HOT 1
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- 我长期研究和改进GAN,如果对GAN或者深度学习感兴趣的可以联系我,联系方式,wechat: lovedaixiaobaby
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