Mar 25, 2021 · 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 …

Jun 2, 2023 · Variational autoencoders, which are a type of neural network, are introduced in this study as a means to virtually increase the sample size of clinical studies and reduce costs, time, dropouts, and ethical concerns. The efficiency of variational autoencoders in data augmentation is proven through simulations of several scenarios.

Mar 1, 2021 · In this paper, we present a data augmentation technique to reproduce crash data. Variational Autoencoder (VAE) was used to generate millions of crash samples from only a limited number of training data. The generated data was compared to real data from different statistical standpoints and similarity was reported.

Sep 25, 2021 · We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting (A code is available at https://github.com/clementchadebec/Data_Augmentation_with_VAE-DALI).

Dec 9, 2024 · We propose to use variational autoencoders (VAE) which are known as a powerful tool for synthetic data generation, offering an interesting approach to modeling and capturing latent representations of complex distributions.

To improve the data augmentation performance of complex processes with limited time-series data, this article introduces a novel VSG method based on time conditional VAE (TimeCVAE). By incorporating labeled data into the VSG process, the generator ensures that the generated virtual samples are closer to the distribution of real samples.

Aug 30, 2024 · We propose an innovative approach that applies data augmentation in the latent space, rather than directly manipulating pixel values. This method utilizes a Variational Autoen- coder, integrated with a pretrained image model, to facilitate the data augmentation process in a more abstract and feature-rich latent space.

Aug 12, 2022 · Data Augmentation techniques enhance the generalizability of the deep learning model by using the original data efficiently. Standard data augmentation techniques like random translations, flips, and rotations, and the addition of Gaussian noise generates authentic but …

Among various methods used for data augmentation, Variational Autoencoders (VAEs) have emerged as a powerful tool that not only aids in generating synthetic data but also captures the underlying distribution of data effectively.

Apr 30, 2021 · 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.