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- W3104322022 abstract "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." @default.
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- W3104322022 date "2020-11-19" @default.
- W3104322022 modified "2023-09-27" @default.
- W3104322022 title "Differentiable Data Augmentation with Kornia." @default.
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