Can we use the library kornia to learn data augmentations?


I have just discovered the Kornia library which is apparently a differentiable computer vision library for PyTorch. Since the computer vision operations in kornia are differentiable, Can we use Kornia to learn data augmentation during the training of a neural network or it’s just meant such that these operations can run on a GPU.

Example: If I use kornia’s augmentation module with randomly initialized parameters. Can I train the network in such a way where these parameters can be updated during backprop?

Hi, did you come across any resource for this? I am also looking for something similar.