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.

翻译：在本文中,我们审查了空间(2D)和体积(3D)加压器的Kornia可不同数据增强模块(DDA),该模块利用了来自Kornia的不同计算机视觉解决方案,目的是将数据增强管道和战略与现有的PyTorch组件(例如,可差异性自动升级,优化)结合起来。此外,我们提供了比较不同的DA框架的基准,并简要审查了利用Kornia DDA的一些方法。