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In your paper you mention diversity augmentation by giving shuffled tiles of the original image to the generator ; have you implemented this anywhere in github?
Thank you.
The text was updated successfully, but these errors were encountered:
Yes. You could refer to util/random_tile.py to produce enough images composed by random tiles and then feed them to the well-trained models to yield diverse results.
Hi,
In your paper you mention diversity augmentation by giving shuffled tiles of the original image to the generator ; have you implemented this anywhere in github?
Thank you.
The text was updated successfully, but these errors were encountered: