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20192026
most citedData Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

103 citations

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stat.ML2021★ 103 cited

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

Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos +1

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 aut…

stat.ML2021

Data Augmentation with Variational Autoencoders and Manifold Sampling

Clément Chadebec, Stéphanie Allassonnière

We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting. This method reveals particularly well suited to perform data aug…