Hi, I am wondering how to use DiffAugment in an image translation task while maintaining explainability of the feature maps in the generator?
I am trying to apply DiffAugment with the policy of 'color, translation, cutout' with CUT and CycleGAN models in my research project. I am still exploring CycleGAN, but, with CUT, I observe the augmentation can delay domination of the discriminator while obtaining lower FID when other hyperparameters are left unchanged. However, the feature maps in the generator get "augmented". They become superimposed of multiple shifted maps, which might reduce the potential of other downstream tasks of interest that require explainability. I wonder if there's any suggestion that you can provide.
Hi, I am wondering how to use DiffAugment in an image translation task while maintaining explainability of the feature maps in the generator?
I am trying to apply DiffAugment with the policy of 'color, translation, cutout' with CUT and CycleGAN models in my research project. I am still exploring CycleGAN, but, with CUT, I observe the augmentation can delay domination of the discriminator while obtaining lower FID when other hyperparameters are left unchanged. However, the feature maps in the generator get "augmented". They become superimposed of multiple shifted maps, which might reduce the potential of other downstream tasks of interest that require explainability. I wonder if there's any suggestion that you can provide.
Thank you for creating these methods : D