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2019 | OriginalPaper | Chapter

Learning Interpretable Disentangled Representations Using Adversarial VAEs

Authors : Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of \(81.50\%\) in terms of disentanglement, \(11.60\%\) in clustering, and \(2\%\) in supervised classification with a few amount of labeled data.
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Metadata
Title
Learning Interpretable Disentangled Representations Using Adversarial VAEs
Authors
Mhd Hasan Sarhan
Abouzar Eslami
Nassir Navab
Shadi Albarqouni
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_5

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