Comments (5)
igul222
commented on July 20, 2024
2
That's not a problem, your charts look okay. The generator loss is actually meaningless by itself, since the critic's loss is invariant to any constant offset (I.e. A critic F(x) and a critic F(x)+c will perform the same under the critic objective). This means that constant offset will randomly vary up and down (and the generator loss with it). So in general you should pay attention only to the critic loss (actually, the negative of that, if you want your loss curve to look more like the typical supervised curves).
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From: wchen342 <[email protected]<mailto:[email protected]>>
Sent: Wednesday, April 26, 2017 8:56 PM
Subject: [igul222/improved_wgan_training] How to interpolate the losses? (#17)
To: igul222/improved_wgan_training <[email protected]<mailto:[email protected]>>
Cc: Subscribed <[email protected]<mailto:[email protected]>>
When I tried wgan-gp on my own problems, sometimes I got very unbalanced losses (e.g. the loss of discriminator is high, but the loss of generator is around 0. Seethis<https://github.com/wchen342/misc/blob/master/Screenshot_20170426_145419.png>). What does this mean? Does it mean the generator is too good?
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wchen342
commented on July 20, 2024
That means sense. Thanks!
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IPNUISTlegal
commented on July 20, 2024
@igul222
@wchen342
They are WGAN-GP loss function.
I am confused that the discriminator loss does not convergence to a certain value and crazy up and down swing！
I am noob in GAN. thx!
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IronLavender
commented on July 20, 2024
They are WGAN-GP loss function.
I am confused that the discriminator loss does not convergence to a certain value and crazy up and down swing！
I am noob in GAN. thx!
Hello, I am thinking how to draw the loss. Sorry it's a little hard for me because these 2 loss should be seperate and in different loops. Will you share the related code to me? Thank you very much.
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PGCJ
commented on July 20, 2024
Hello，I am noob in GAN.
I want to konw that what looks normal in WGAN_GP's loss? between Dloss and Gloss.
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Related Issues (20)
- o._shape = TensorShape(new_shape) caused error in inception_score.py HOT 1
- Why the gradient penalty item decreases to zero and then grows to infinity ?
- This code is outdated seriously HOT 3
- inception_score.py: fixed the issue of ValueError "Cannot iterate over a shape with unknown rank"
- inception_score.py: ValueError in the method _init_inception() HOT 1
- Could it be possible to make the trained GAN publicly available?
- Mismatch between code and paper in the gradient penalty algorithm HOT 1
- Questions about the loss
- AttributeError: module '_pickle' has no attribute 'HIGHEST_PROTOCOL' HOT 1
- Error Conv2DCustomBackpropFilterOp only supports NHWC HOT 2
- Question of DEVICE in the gan_cifar10_resnet.py
- how to run it？
- Critic loss curve
- a question about loss
- reproducing inception score on gan_cifar.py HOT 2
- If I intend to calculate gradient penalty for two dataset in differet dimension, what should I do?
- gan_mnist.py's ERROR HOT 1
- Query: WGAN-GP FID SCORE (PyTorch)
- Wire gide
- Conv2DCustomBackpropInputOp only supports NHWC
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