  In this paper we present a review of the Kornia differentiable data
augmentation (DDA) module for both for spatial (2D) and volumetric (3D)
tensors. This module leverages differentiable computer vision solutions from
Kornia, with an aim of integrating data augmentation (DA) pipelines and
strategies to existing PyTorch components (e.g. autograd for differentiability,
optim for optimization). In addition, we provide a benchmark comparing
different DA frameworks and a short review for a number of approaches that make
use of Kornia DDA.
