Comments (3)
I tested the given pre-trained model (mit-han-lab:DiffAugment-biggan-cifar10-0.1.pth).
This model also shows the augmentation leakage issue. Is it okay to use this ill-behaved model as the best model to report the FID score?
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BigGAN seems to expose some unique patterns when the model is still at an early stage of training when collapsed. Also means that there is still huge space for improvements.
from data-efficient-gans.
Thanks for the reply. The best FID score of BigGAN + DiffAug with 10% of samples might be 29.xx without the collapse or leakage, then, as you say, there might be much room for improvement.
from data-efficient-gans.
Related Issues (20)
- generate_gif.py error:Cannot handle this data type:(1,1,64),|u1
- Why isn't DiffAugment used as a layer in the discriminator?
- Unavailable weights for pre-trained models
- How can we train diffaugment-styleGAN2 on our own dataset
- Training set image resolution HOT 4
- Cannot download checkpoints HOT 2
- Would you please advice on how to use your code?
- requirements HOT 1
- Generate images from a grayscale trained pkl file HOT 2
- How to train DiffAugment-biggan with the self-made dataset? HOT 3
- Network pickle not saving!
- the D and G values for updating HOT 9
- How to use DiffAugment in Image to Image Translation? HOT 2
- NotImplementedError: Cannot convert a symbolic Tensor (Inputs/minibatch_gpu_in:0) to a numpy array. HOT 3
- How do you calculate accuracy in paper? HOT 1
- How can we use generate.py file to load our own trained models? HOT 1
- RuntimeError: No such operator aten::cudnn_convolution_backward_weight HOT 1
- how to deal with 'check_hostname requires server_hostname'
- The difference between the generated image and the training image is too large
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