Paper ID: 2501.08009 • Published Jan 14, 2025
Tutorial: VAE as an inference paradigm for neuroimaging
C. Vázquez-García, F. J. Martínez-Murcia, F. Segovia Román, Juan M. Górriz Sáez
TL;DR
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In this tutorial, we explore Variational Autoencoders (VAEs), an essential
framework for unsupervised learning, particularly suited for high-dimensional
datasets such as neuroimaging. By integrating deep learning with Bayesian
inference, VAEs enable the generation of interpretable latent representations.
This tutorial outlines the theoretical foundations of VAEs, addresses practical
challenges such as convergence issues and over-fitting, and discusses
strategies like the reparameterization trick and hyperparameter optimization.
We also highlight key applications of VAEs in neuroimaging, demonstrating their
potential to uncover meaningful patterns, including those associated with
neurodegenerative processes, and their broader implications for analyzing
complex brain data.