Data Augmentation with Variational Autoencoders and Manifold Sampling

Clément Chadebec; Stéphanie Allassonnière

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arXiv · 2103.13751

Data Augmentation with Variational Autoencoders and Manifold Sampling

Clément Chadebec·Stéphanie Allassonnière

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Abstract

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 augmentation in such a low data regime and is validated across various standard and real-life data sets. In particular, this scheme allows to greatly improve classification results on the OASIS database where balanced accuracy jumps from 80.7% for a classifier trained with the raw data to 88.6% when trained only with the synthetic data generated by our method. Such results were also observed on 3 standard data sets and with other classifiers. A code is available at https://github.com/clementchadebec/Data_Augmentation_with_VAE-DALI.

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Clément Chadebec, Stéphanie Allassonnière. 2021-03-25. Data Augmentation with Variational Autoencoders and Manifold Sampling. https://arxiv.org/abs/2103.13751

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