Add aesthetic-rating aided dataset support into the discriminator loss function.
Add a function that could penalize the discriminator for thinking an image from the dataset is "more real" than one which has a higher aesthetic-score rating (could be an aesthetic score system beetwen 1 to 5 or 1 to 10). We could organize a dataset based on aesthetic scores by seperating them into seperate folders (example : folders named 1,2,3,4,5,6,7,8,9,10 , for 1-10 aesthetic score rating).
This would greatly help use-cases where you have a lot of images for a dataset but only a select few are good enough in quality and creativity, but you still wanna keep the diversity of a much larger dataset.
Add aesthetic-rating aided dataset support into the discriminator loss function. Add a function that could penalize the discriminator for thinking an image from the dataset is "more real" than one which has a higher aesthetic-score rating (could be an aesthetic score system beetwen 1 to 5 or 1 to 10). We could organize a dataset based on aesthetic scores by seperating them into seperate folders (example : folders named 1,2,3,4,5,6,7,8,9,10 , for 1-10 aesthetic score rating).
This would greatly help use-cases where you have a lot of images for a dataset but only a select few are good enough in quality and creativity, but you still wanna keep the diversity of a much larger dataset.