Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

@article{Chadebec2022DataAI,
  title={Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder},
  author={Cl'ement Chadebec and Elina Thibeau-Sutre and Ninon Burgos and St{\'e}phanie Allassonni{\`e}re},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2022},
  volume={PP}
}
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder (VAE). Our approach combines the proposal of 1) a new VAE model, the latent space of which is modeled as a Riemannian manifold and which combines both Riemannian metric learning and normalizing flows and 2) a new generation scheme which produces more meaningful samples especially in the context of small data… 

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