Publication
Differentiable data augmentation with Kornia
Conference Article
Conference
Workshop on Differentiable Vision, Graphics, and Physics Applied to Machine Learning (DiffCVGP)
Edition
1st
Doc link
https://montrealrobotics.ca/diffcvgp/assets/papers/8.pdf
File
Authors
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Shi, Jian
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Riba Pi, Edgar
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Mishkin, Dmytro
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Moreno Noguer, Francesc
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Nicolaou, Anguelos
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.
Categories
computer vision.
Author keywords
computer visio, differentiable data augmentation, deep learning
Scientific reference
J. Shi, E. Riba, D. Mishkin, F. Moreno-Noguer and A. Nicolaou. Differentiable data augmentation with Kornia, 1st Workshop on Differentiable Vision, Graphics, and Physics Applied to Machine Learning, 2020, Online.
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